Classification device, classification method, and classification program

Abstract

The present invention provides a classification device capable of classifying psychological maturity into a predetermined class. A classification device according to the present disclosure includes an information acquisition unit and a classification unit. The information acquisition unit acquires psychological evaluation information for a subject. The classification unit classifies the psychological maturity of the subject into a predetermined class related to psychological maturity based on the psychological evaluation information. The classification is performed in accordance with a classification model that outputs the predetermined class when the psychological evaluation information is input.

Description

Classification device, classification method, and classification program

[0001] The present disclosure relates to a classification device, a classification method, and a classification program.

[0002] Non-Patent Document 1 mentions the relationship between psychological maturity and academic ability.

[0003] Elisa Camps and Fabia Morales-Vives, The Contributions of Psychological Maturity and Personality in the Prediction of Adolescent Academic Achievement, International Journal of Educational Psychology, Vol. 2 No. 3 October 2013 pp. 246-271.

[0004] In various fields including the educational field described in Non-Patent Document 1, it has been reported that psychological maturity affects various aspects of humans. Therefore, in order to understand the influence of psychological maturity on various aspects of humans, classification of psychological maturity into predetermined classes is required.

[0005] Accordingly, an object of the present disclosure is to provide a classification device, a classification method, and a classification program that are capable of classifying psychological maturity into predetermined classes.

[0006] In order to achieve the above object, the classification device of the present disclosure includes an information acquisition unit and a classification unit, wherein the information acquisition unit acquires psychological evaluation information of a subject, the psychological evaluation information includes psychological evaluation information related to at least one of mental health and adaptation, and psychological evaluation information related to at least one of development and lifespan, the classification unit classifies the psychological maturity of the subject into a predetermined class related to psychological maturity based on the psychological evaluation information, and the classification is performed by a classification model that outputs the predetermined class when the psychological evaluation information is input to the model.

[0007] The classification method of this disclosure includes an information acquisition step and a classification step, wherein the information acquisition step acquires psychological evaluation information of a subject, the psychological evaluation information includes psychological evaluation information relating to at least one of mental health and adaptation, and psychological evaluation information relating to at least one of development and lifespan, the classification step classifies the subject's psychological maturity into predetermined classes relating to psychological maturity based on the psychological evaluation information, and the classification is performed by a classification model that outputs the predetermined class when the psychological evaluation information is input, and each of the above steps is performed by a computer.

[0008] The classification program of this disclosure includes an information acquisition procedure and a classification procedure, wherein the information acquisition procedure acquires psychological evaluation information of a subject, the psychological evaluation information includes psychological evaluation information relating to at least one of mental health and adaptation, and psychological evaluation information relating to at least one of development and lifespan, the classification procedure classifies the subject's psychological maturity into a predetermined class relating to psychological maturity based on the psychological evaluation information, and the classification is performed by a classification model that outputs the predetermined class when the psychological evaluation information is input, and the program is designed to cause a computer to perform each of the above procedures.

[0009] According to this disclosure, psychological maturity can be classified into predetermined classes.

[0010] Figure 1 is a block diagram showing the configuration of an example of the classification device of this disclosure. Figure 2 is a block diagram showing an example of the hardware configuration of the classification device of this disclosure. Figure 3 is a flowchart showing an example of the process according to the classification method of this disclosure. Figure 4 is a flowchart showing another example of the process according to the classification method of this disclosure. Figure 5 is a table showing the subjective age and demographic data of the subjects. Figure 6(A) is a graph showing the results of cluster classification using the Louvaiin method. Figure 6(B) is a table showing the proportion of subjects in each cluster. Figure 7(A) is a graph showing the distribution of those in their 20s (20s), 30s (30s), 40s (40s), 50s (50s), and 60s and over (60s) in each cluster. Figure 7(B) is a graph showing the distribution of young, middle-aged, and elderly in each cluster. Figure 8 is a table showing the subjective age in each cluster. Figure 9(A) shows the evaluation results of a classification model using 79 items. Figure 9(B) shows the evaluation results of a classification model using 10 items. Figure 9(C) shows the evaluation results of the classification model using five items.

[0011] The embodiments of this disclosure will be described below with reference to the drawings. This disclosure is not limited to the embodiments described below. In the following drawings, the same parts are denoted by the same reference numerals. Furthermore, unless otherwise specified, the descriptions of each embodiment can be used interchangeably with those of the other, and unless otherwise specified, the configurations of each embodiment can be combined. In this disclosure, each drawing may correspond to one or more embodiments.

[0012] [Embodiment 1] An example of the configuration of the classification device of the present disclosure will be described with reference to Figure 1. Figure 1 is a block diagram showing an example of the configuration of the classification device 10 of the present disclosure (hereinafter also referred to as the device 10). As shown in Figure 1, the device 10 includes an information acquisition unit 11 and a classification unit 12. The device 10 may also include, for example, an input unit, an output unit, a display unit and / or a storage unit, although these are not shown. The information acquisition unit 11 and the classification unit 12 are capable of executing, for example, the information acquisition procedure and the classification procedure in the classification program of the present disclosure, which will be described later.

[0013] The device 10 may be, for example, a single device including the aforementioned parts, or it may be a device in which each of the aforementioned parts can be connected via a communication network. Furthermore, the device 10 can be connected to an external device described later via the communication network. The communication network is not particularly limited and can use a known network, for example, it may be wired or wireless. Examples of the communication network include the Internet, WWW (World Wide Web), telephone line, LAN (Local Area Network), SAN (Storage Area Network), DTN (Delay Tolerant Networking), LPWA (Low Power Wide Area), L5G (Local 5G), etc. Examples of wireless communication include Wi-Fi®, Bluetooth®, Local 5G, LPWA, etc. The aforementioned wireless communication may be in the form of direct communication between devices (Ad Hoc communication), infrastructure communication, or indirect communication via an access point. The device 10 may, for example, be incorporated into a server as a system. Alternatively, the device 10 may be, for example, a personal computer (PC, e.g., desktop or notebook), smartphone, or tablet terminal on which the program disclosed herein is installed. The device 10 may also be in the form of cloud computing or edge computing, for example, in which at least one of the aforementioned parts is on a server and the other parts are on a terminal.

[0014] Figure 2 illustrates a block diagram of the hardware configuration of the device 10. The device 10 includes, for example, a central processing unit (CPU, GPU, etc.) 101, memory 102, bus 103, storage device 104, input device 105, output device 106, communication device 107, etc. Each part of the device 10 is interconnected via the bus 103 through its respective interface (I / F).

[0015] The central processing unit 101 operates in coordination with other components via controllers (system controller, I / O controller, etc.) and is responsible for the overall control of the device 10. In the device 10, the central processing unit 101 executes, for example, the program of this disclosure (classification program) and other programs, and also reads and writes various types of information. Specifically, for example, the central processing unit 101 functions as an information acquisition unit 11 and a classification unit 12. The device 10 may also include other computing devices such as a CPU, GPU (Graphics Processing Unit), APU (Accelerated Processing Unit), or a combination thereof as its computing device.

[0016] Bus 103 can also be connected to external devices, for example. Examples of such external devices include external storage devices (external databases, etc.), printers, external input devices, external display devices, and external imaging devices. The device 10 can be connected to an external network (the aforementioned communication network) via a communication device 107 connected to bus 103, for example, and can also be connected to other devices via the external network.

[0017] Memory 102 may be, for example, main memory. When the central processing unit 101 performs processing, memory 102 reads various operational programs, such as the program of this disclosure, stored in the storage device 104 (described later), and the central processing unit 101 receives data from memory 102 and executes the program. The main memory may be, for example, RAM (random access memory). Alternatively, memory 102 may be, for example, ROM (read-only memory).

[0018] The storage device 104 is also called an auxiliary storage device, for example, in relation to the main memory (primary memory). As described above, the storage device 104 stores an operating program including the program of this disclosure. The storage device 104 may be, for example, a combination of a recording medium and a drive for reading and writing to the recording medium. The recording medium is not particularly limited and may be internal or external, for example, an HD (hard disk), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, memory card, etc. The storage device 104 may be, for example, a hard disk drive (HDD) in which the recording medium and the drive are integrated, or a solid state drive (SSD). If the device 10 includes the storage unit, for example, the storage device 104 functions as the storage unit. The storage unit can record, for example, various types of information, which will be described later.

[0019] In this device 10, the memory 102 and storage device 104 can also store various types of information, such as log information, information obtained from an external database (not shown) or external devices, information generated by this device 10, and information used by this device 10 when executing processing. In this case, the memory 102 and storage device 104 may store, for example, user information of this device 10. At least some of the information may be stored on an external server other than the memory 102 and storage device 104, or it may be stored in a distributed manner across multiple terminals using blockchain technology or the like.

[0020] The device 10 further includes, for example, an input device 105 and an output device 106. The input device 105 may include, for example, a pointing device such as a touch panel, trackpad, or mouse; a keyboard; imaging means such as a camera or scanner; a card reader such as an IC card reader or magnetic card reader; an audio input means such as a microphone; and so on. The output device 106 may include, for example, a display device such as an LED display or liquid crystal display; an audio output device such as a speaker; a printer; and so on. In this embodiment 1, the input device 105 and the output device 106 are configured separately, but the input device 105 and the output device 106 may be configured as an integrated unit, such as a touch panel display.

[0021] An example of processing using the classification method of this disclosure will be explained in more detail with reference to Figure 3. Figure 3 is a flowchart showing an example of each step of the classification method of this disclosure. The classification method of this disclosure can be implemented, for example, using the apparatus 10 of this disclosure shown in Figure 1 or Figure 2. However, the classification method of this disclosure is not limited to, for example, the method using the apparatus 10 of this disclosure.

[0022] The information acquisition unit 11 acquires psychological evaluation information of the subject (S11, information acquisition process).

[0023] The aforementioned psychological evaluation information is, for example, information that evaluates the subject's psychology using a psychological scale. The aforementioned psychological evaluation information may include, for example, psychological score information that scores the subject's psychology using the psychological scale. The evaluation of the subject's psychology using the psychological scale may be, for example, an evaluation based on a questionnaire related to the psychological scale.

[0024] The aforementioned psychological assessment information includes psychological assessment information relating to at least one of mental health and adaptation, and psychological assessment information relating to at least one of development and lifespan.

[0025] More specifically, the psychological evaluation information may include, for example, psychological evaluation information regarding the control of behavioral inhibition, psychological evaluation information regarding mental health, psychological evaluation information regarding physical ailments, psychological evaluation information regarding composure and maturity, and psychological evaluation information regarding generational achievement. These five items of psychological evaluation information correspond to, for example, the psychological evaluation information used in the classification model that uses the five explanatory variables in the embodiment described later.

[0026] The aforementioned psychological evaluation information may, for example, be information obtained by evaluating the subject's psychology using any of the evaluation items related to the psychological scale shown in Table 1 below, or it may be information obtained by evaluating the subject's psychology using any of the detailed evaluation items related to the psychological scale shown in Table 1 below.

[0027]

[0028] The evaluation of each psychological scale in Table 1 above may be performed, for example, based on the methods described in References 1 to 11 below. However, the evaluation method for each psychological scale in Table 1 above is not limited to the evaluation methods described in References 1 to 11 below. Specifically, the "Effortful Control Scale for Adults" may be evaluated, for example, based on the method described in Reference 1 below. The "Kessler Psychological Distress Scale (K6)" may be evaluated, for example, based on the method described in Reference 2 below. The "Revised UCLA Loneliness Scale" may be evaluated, for example, based on the method described in Reference 3 below. The "Life Satisfaction Scale (SWLS)" may be evaluated, for example, based on the method described in Reference 4 below. The "Items included in the NILS-LSA Questionnaire" may be evaluated, for example, based on the method described in Reference 5 below. The "Vitality Scale" may be evaluated, for example, based on the method described in Reference 6 below. The "Multidimensional Future Perspective Scale" may be evaluated, for example, based on the method described in Reference 7 below. The “Subjective Experience of Aging Scale” may be evaluated, for example, based on the method described in Reference 8 below. The “Age-Related Change Awareness Scale (10-item Short Version)” may be evaluated, for example, based on the method described in Reference 9 below. The “Revised Generational Transparency Scale (JGS-R)” may be evaluated, for example, based on the method described in Reference 10 below. The “Japanese Version of the EH Erikson Developmental Task Achievement Scale” may be evaluated, for example, based on the method described in Reference 11 below. 1. Yamagata, S., Takahashi, Y., Shigemasu, K., Ono, Y., & Kijima, N. (2005). Development and validation of Japanese version of Effortful Control Scale for Adults. The Japanese Journal of Personality, 14(1), 30-41. 2. Furukawa, TA, Kessler, RC, Slade, T., & Andrews, G. (2003).The performance of the K6 and K10 screening scales for psychological distress in the Australian National Survey of Mental Health and Well-Being. Psychological Medicine, 33(2), 357-362. 3. Kudoh, T., & Nishikawa, M. (1983). A study of the feeling of loneliness (I). The Japanese Journal of Experimental Social Psychology, 22(2), 99-108. 4. Sumino, Z. (1994). Development of the Japanese version of the Satisfaction With Life Scale. Proceedings of the 36th Annual Meeting of the Japanese Association of Educational Psychology, 36, 192. 5. Fukukawa, Y., Tsuboi, S., Niino, N., Ando, F., Kosugi, S., & Shimokata, H. (2002). Stress, social exchanges, and depressive symptoms in Japanese middle-aged and elderly adults: Positive and negative effects of familial relationships on psychological health. The Japanese Journal of Developmental Psychology, 13(1), 42-50. 6. Fukui, M., & Shimizu, K. (2015). Development of the Vitality Scale (VS) for undergraduates and adults.The Japanese Journal of Personality, 24(2), 147-150. 7. Shiraishi, N., & Horiuchi, T. (2022). Development of a Japanese version of the Multidimensional Future Time Perspective (MFTP) Scale, and investigation of its reliability and validity. The Japanese Journal of Personality, 31(2), 159-162. 8. Wakamoto, J., & Muto, T. (2006). Subjective experiences of aging in middle and late adulthood. The Japanese Journal of Developmental Psychology, 17(2), 84-93. 9. Shiraishi, N., Horiuchi, T., & Brothers, A. (2024). Development of a Japanese version of the Awareness of Age-Related Change Scale (AARC-10 SF) and investigation of its reliability and validity. The Japanese Journal of Psychology, 95(4), 304-313. 10. Murayama, S., Nakatani, Y., Wakabayashi, R., Takeuchi, R., & Matsuo, M. (2022). Development of Revised Japanese Version of Generativity Scale (JGS-R) and investigation of its reliability and validity. The Japanese Journal of Personality, 30(2), 151-160. 11. Kidan, N.(2020). Effectiveness of a nursing intervention program designed to help older living adults with early dementia gain life integrity. Journal of Japan Academy of Gerontological Nursing, 25(1), 68-77.

[0029] The aforementioned psychological assessment information may further include, for example, psychological assessment information relating to at least one of self-concept and identity. According to this disclosure, for example, psychological maturity can be classified into predetermined classes with greater accuracy.

[0030] In this case, the psychological evaluation information may be, for example, information obtained by evaluating the subject's psychology using any of the evaluation items related to the psychological scale shown in Table 2 below, or information obtained by evaluating it using any of the detailed evaluation items related to the psychological scale shown in Table 2 below.

[0031] More specifically, the psychological evaluation information may include psychological evaluation information relating to the control of behavioral inhibition, psychological evaluation information relating to mental health, psychological evaluation information relating to physical ailments, psychological evaluation information relating to composure and maturity, psychological evaluation information relating to generational achievement, psychological evaluation information relating to flexibility, psychological evaluation information relating to withdrawal, psychological evaluation information relating to competence and superiority, psychological evaluation information relating to loss, and psychological evaluation information relating to generational behavior. These ten items of psychological evaluation information correspond, for example, to the psychological evaluation information used in the classification model using the ten explanatory variables in the embodiment described later.

[0032]

[0033] The evaluation of each psychological scale in Table 2 above may be performed, for example, based on the methods described in references 12 to 16 below. However, the evaluation methods for each psychological scale in Table 2 above are not limited to evaluations based on the methods described in references 12 to 16 below. Specifically, the "Rosenberg Self-Esteem Scale" may be evaluated, for example, based on the method described in reference 12 below. The "Narcissistic Personality Scale (NPS) Short Version" may be evaluated, for example, based on the method described in reference 13 below. The "Japanese Translation of the Self-Concept Clarity Scale" may be evaluated, for example, based on the method described in reference 14 below. The "Multidimensional Ego Identity Scale (MEIS)" may be evaluated, for example, based on the method described in reference 15 below. The "Revised Mutually Independent-Mutually Cooperative Self-View Scale" may be evaluated, for example, based on the method described in reference 16 below.12. Sakurai, S. (2000). Investigation of the Japanese version of Rosenberg's Self-Esteem Scale. Bulletin of Tsukuba Developmental and Clinical Psychology, 12, 65-71. 13. Tani, F. (2006). Development of a short version of the Narcissistic Personality Scale (NPS) [Poster presentation]. The 48th Annual Meeting of the Japanese Association of Educational Psychology, 409. 14. Tokunaga, Y., & Horiuchi, T. (2012). Development of a Japanese version of the Self-Concept Clarity (SCC) Scale. The Japanese Journal of Personality, 20(3), 193-203. 15. Tani, F. (2001). Structure of the sense of identity in adolescents. The Japanese Journal of Educational Psychology, 49(3), 265-273. 16. Takata, T. (1999). Developmental process of independent and interdependent self-construal in Japanese culture. The Japanese Journal of Educational Psychology, 47(4), 480-489.

[0034] The information acquisition unit 11 may, for example, acquire the psychological evaluation information from the memory 102 or storage device 104 of the device 10, or it may acquire it from another device other than the device 10.

[0035] The information acquisition unit 11 may, for example, acquire the results of the questionnaire regarding the psychological scale and acquire the psychological evaluation information calculated from the results of the questionnaire as the psychological evaluation information.

[0036] The classification unit classifies the psychological maturity of the subject into predetermined classes related to psychological maturity based on the psychological evaluation information, and the classification is performed by a classification model that outputs the predetermined class when the psychological evaluation information is input (S12, classification step).

[0037] The aforementioned psychological maturity level is, for example, a degree of psychological maturity. The degree of psychological maturity can be, for example, two or more levels. The degree of psychological maturity can be, for example, three levels such as low, medium, and high. The aforementioned psychological maturity means, for example, maturity in psychological aspects. The aforementioned psychological maturity can be, for example, improved self-awareness, improved flexibility of thought, improved ego resilience, and emotional control. The effects of achieving the aforementioned psychological maturity can be, for example, improved adaptability to stress, improved flexibility to change, maintenance of physical and psychological health, achievement of successful aging, improved academic ability, improved problem-solving ability, suppression of the onset of depression, improved self-awareness, improved emotional control, and improved interpersonal skills. The effects of achieving the aforementioned successful aging can be, for example, an extension of healthy life expectancy and a reduction in the gap between healthy life expectancy and average life expectancy.

[0038] The aforementioned level of psychological maturity may be evaluated based, for example, on reference literature concerning psychological maturity. The aforementioned level of psychological maturity may be evaluated as low, medium, or high based on reference literature concerning psychological maturity. Examples of reference literature concerning psychological maturity include the references by Eryilmaz, A., & Uzun, AE (2024). Alpha Psychiatry, 25, 101-110., and the references by Carreno, D. F., Eisenbeck, N., Greville, J., & Wong, PTP (2023). Cross-cultural psychometric analysis of the Mature Happiness Scale-Revised: Mature happiness, psychological inflexibility, and the PERMA model. Journal of Happiness Studies, 24(3), 1075-1099.

[0039] The predetermined class is, for example, a class relating to psychological maturity. Each of the predetermined classes is, for example, associated with a level of psychological maturity. The predetermined class may include, for example, a class for high psychological maturity, a class for medium psychological maturity, and a class for low psychological maturity. The high psychological maturity class may include, for example, classes 2 and 5 described later. The medium psychological maturity class may include, for example, classes 1, 3, and 4 described later. The low psychological maturity class may include, for example, class 6 described later.

[0040] The predetermined classes may, for example, be classes relating to psychological maturity and subjective age. In this case, each class of the predetermined classes is associated with, for example, the psychological maturity and the subjective age. Furthermore, the predetermined classes may include, for example, the following five classes. Specifically, the predetermined classes may include, for example, a class with low psychological maturity and who perceives themselves as elderly, a class with moderate psychological maturity and who desires to be young, a class with moderate psychological maturity and who perceives themselves as young, a class with moderate psychological maturity and whose perceived age coincides with their chronological age, and a class with high psychological maturity and who perceives themselves as young.

[0041] The predetermined class may include, for example, the following classes 1 to 6 if the predetermined class is a class relating to psychological maturity and subjective age. Class 1 is, for example, a class with moderate psychological maturity and a desire to be young. Class 2 is, for example, a class with high psychological maturity, high ratings for self-consistency and interpersonal skills in psychological evaluation information, and a self-perception of being young. Class 3 is, for example, a class with moderate psychological maturity and a self-perception of age that matches chronological age. Class 4 is, for example, a class with moderate psychological maturity and a self-perception of being young. Class 5 is, for example, a class with high psychological maturity, high ratings for will to live life to the fullest and intellectual curiosity in psychological evaluation information, and a self-perception of being young. Class 6 is, for example, a class with low psychological maturity and a self-perception of being elderly.

[0042] The classification model may be, for example, a model based on machine learning. The classification model may be, for example, a trained model generated by machine learning using training data in which the psychological evaluation information is used as an explanatory variable and the predetermined class is used as an objective variable. Examples of the machine learning include a gradient boosting method. The explanatory variable may be appropriately selected by, for example, a variable selection method. Examples of the variable selection method include Recursive Feature Elimination (RFE). In this case, the psychological evaluation information may include, for example, psychological evaluation information on at least one of mental health and adaptation, and psychological evaluation information on at least one of development and lifespan. Further, the psychological evaluation information may further include, for example, psychological evaluation information on at least one of self-concept and identity.

[0043] The classification model is constructed by, for example, machine learning using training data in which the psychological evaluation information is used as an explanatory variable and a predetermined class related to psychological maturity is used as an objective variable, and may be a model for causing a computer to function so as to output the predetermined class related to psychological maturity as an objective variable when the psychological evaluation information is input as an explanatory variable. In this case, the psychological evaluation information may include, for example, psychological evaluation information on at least one of mental health and adaptation, and psychological evaluation information on at least one of development and lifespan. Further, the psychological evaluation information may further include, for example, psychological evaluation information on at least one of self-concept and identity.

[0044] The method for generating the classification model includes, for example, a step of acquiring training data and a step of generating the classification model. Each of the steps in the method for generating the classification model is performed, for example, by a computer. The training data acquisition step involves acquiring training data in which the psychological evaluation information is used as an explanatory variable and a predetermined class relating to psychological maturity is used as the target variable. The classification model generation step involves generating a classification model using machine learning with the training data, for example, which outputs a predetermined class relating to psychological maturity as the target variable when the psychological evaluation information is input as an explanatory variable. In this case, the psychological evaluation information may include, for example, psychological evaluation information relating to at least one of mental health and adaptation, and psychological evaluation information relating to at least one of development and lifespan. Furthermore, the psychological evaluation information may further include, for example, psychological evaluation information relating to at least one of self-concept and identity.

[0045] The classification model may be stored, for example, in the memory 102 or storage device 104 of the device 10, or in another device other than the device 10.

[0046] If the device 10 includes an output unit, it may output the predetermined class. The output unit of the device 10 may output the predetermined class to its output device 106, or to an output device of another device other than the device 10.

[0047] The classification program of the present disclosure (hereinafter also referred to as the program of the present disclosure) is a program for causing a computer to execute each step of the aforementioned classification method of the present disclosure. Specifically, the program of the present disclosure is a program for causing a computer to execute an information acquisition procedure and a classification procedure. Further, the program of the present disclosure can also be described as a program that causes a computer to function as an information acquisition procedure and a classification procedure. The program of the present disclosure includes an information acquisition procedure and a classification procedure, wherein the information acquisition procedure acquires psychological evaluation information of a subject, and the classification procedure classifies the psychological maturity of the subject into a predetermined class related to psychological maturity based on the psychological evaluation information, and the classification is performed by a classification model that outputs the predetermined class when the psychological evaluation information is input. The program of the present disclosure can be implemented, for example, using the present apparatus 10 shown in FIG. 1 or FIG. 2. Note that the program of the present disclosure is not limited to, for example, a program that uses the present apparatus 10. For the program of the present disclosure, for example, the descriptions in the method of the present disclosure and the apparatus of the present disclosure can be incorporated. For each of the above procedures, for example, the term "procedure" can be read as "process".

[0048] As described above, according to the classification apparatus of the present disclosure, the information acquisition unit acquires psychological evaluation information of a subject, and the classification unit classifies the psychological maturity of the subject into a predetermined class related to psychological maturity based on the psychological evaluation information, and the classification can be performed by a classification model that outputs the predetermined class when the psychological evaluation information is input. Therefore, according to the present disclosure, psychological maturity can be classified into a predetermined class. Further, according to the present disclosure, for example, since the psychological maturity of the subject is classified into a predetermined class, the subject can easily grasp their own psychological maturity.

[0049] According to this disclosure, for example, by understanding the current degree of psychological maturity, it is possible to motivate individuals to achieve psychological maturity. By achieving psychological maturity, individuals who have received such motivation can enjoy various benefits that come with it. For example, by achieving psychological maturity, motivated individuals can achieve successful aging and, as a result, extend their healthy lifespan. Therefore, according to this disclosure, it is possible to provide various benefits at the family and societal levels, such as reducing medical and nursing care costs, improving the quality of life in old age and beyond, and extending the working period.

[0050] [Embodiment 2] Another example of the classification device of the present disclosure will be described.

[0051] The processing of the information acquisition unit 11 and the classification unit 12 of this disclosure will be described below. The processing of the information acquisition unit 11 and the classification unit 12 of this disclosure is shown, for example, in the flowchart of Figure 4.

[0052] The information acquisition unit 11 further acquires, for example, the subjective age information and attribute information of the subject (S11A, information acquisition step). That is, the information acquisition unit 11 acquires, for example, the subject's psychological evaluation information, subjective age information, and attribute information.

[0053] The subjective age information is, for example, information relating to subjective age. The subjective age is, for example, at least one of the age and life expectancy that the subject subjectively perceives. The subjective age is, for example, a different age from the chronological age. The subjective age information may be, for example, information relating to the subjective age itself, or information relating to the age difference (also called "inconsistency" in psychology) obtained by subtracting the subjective age from the chronological age, or information relating to the value obtained by dividing the age difference by the chronological age (also called "proportional inconsistency" in psychology), or a combination thereof. The subjective age information may be, for example, if the age difference is a positive number, it means that the subject perceives themselves as younger than their chronological age, and if the age difference is a negative number, it means that the subject perceives themselves as older than their chronological age.

[0054] The subjective age information may, for example, be information based on a questionnaire on subjective age. The questionnaire on subjective age may, for example, use the subjective age questions from the Midlife in the United States (MIDUS) longitudinal study. The questionnaire on subjective age may, for example, be based on reference literature that uses the subjective age questions from the MIDUS longitudinal study. References using the subjective age questionnaire items in the aforementioned MIDUS longitudinal study include, for example, the references of Kastenbaum, R. et al. (Kastenbaum, R., Derbin, V., Sabatini, P., & Artt, S. (1972). "The ages of me": Toward personal and interpersonal definitions of functional aging. International Journal of Aging and Human Development, 3(2), 197-211.), and the references of Nakagawa, T. et al. (Nakagawa, T., Gondo, Y., Ishioka, Y., & Masui, Y. (2013). Development of a Japanese version of the Valuation of Life (VOL) scale. The Japanese Journal of Psychology, 84(1), 37-46.).

[0055] The subjective age information may include, for example, at least one of the following: information relating to perceived age, information relating to cognitive age, information relating to desired age, information relating to ideal age, information relating to age of interest, and information relating to subjective life expectancy.

[0056] The perceived age is, for example, the age that the subject normally perceives as their own age. The perceived age may also be, for example, the age answered to the question, "Many people feel older or younger than their actual age. Please write down the age you usually feel you are."

[0057] The aforementioned cognitive age is, for example, the age at which the subject perceives themselves as being seen by others. The aforementioned cognitive age may also be, for example, the age answered to the question, "How old do you think others think you are? Please tell us the age you perceive yourself to be."

[0058] The aforementioned desired age is, for example, the age at which the subject perceives themselves to be seen by others. The aforementioned desired age may also be, for example, the age answered to the question, "At what age would you like others to see you? Please tell us the age you feel you are."

[0059] The aforementioned ideal age is, for example, the age that the subject considers ideal. The aforementioned ideal age may also be, for example, the age answered to the question, "If you could choose an age, what age would you choose? Please tell us the age you feel you are."

[0060] The aforementioned age of interest is, for example, the age that the subject perceives as appropriate for the interests they have. The aforementioned age of interest may also be the age answered to a question such as, "What age group do you feel your interests are roughly the same as? Please tell me the age you perceive yourself to be at."

[0061] The subjective life expectancy is, for example, the ideal life expectancy for the subject. The subjective life expectancy may also be, for example, the period answered to the question, "How long would you like to live from now? Please write down the number of years you feel is necessary." The information regarding the subjective life expectancy may be, for example, the subjective life expectancy itself, or it may be the subjective lifespan obtained by adding the subjective life expectancy to the chronological age.

[0062] The attribute information is, for example, information relating to the attributes of the subject. The attribute information may also be, for example, information relating to demographic data. The demographic data may also be, for example, information based on a questionnaire relating to the demographic data.

[0063] The attribute information may include, for example, at least one of the following: information about gender, information about work history, information about educational background, information about marital status, and information about whether or not the person has children.

[0064] The aforementioned information regarding gender is, for example, information regarding whether or not a person is of a specific gender. The aforementioned information regarding work history is, for example, information regarding whether or not a person is employed. The aforementioned information regarding educational background is, for example, information regarding whether or not a person has an educational background. The aforementioned presence or absence of educational background includes, for example, whether or not a person has graduated from junior high school, high school, university, or graduate school.

[0065] The aforementioned psychological evaluation information may further include, for example, psychological evaluation information relating to at least one of personality and personal characteristics, and psychological evaluation information relating to at least one of skills and competence. In this case, the aforementioned psychological evaluation information may be, for example, information obtained by evaluating the subject's psychology using any of the evaluation items for the psychological scale shown in Table 3 below, or information obtained by evaluating using any of the detailed evaluation items for the psychological scale shown in Table 3 below.

[0066]

[0067] The evaluation of each psychological scale in Table 3 may be performed, for example, based on the methods described in references 17 to 23 below. However, the evaluation method for each psychological scale in Table 3 is not limited to the methods described in references 17 to 23 below. Specifically, the "Ten Item Personality Inventory (TIPI-J)" may be evaluated, for example, based on the method described in reference 17 below. The "Adult Trust Scale" may be evaluated, for example, based on the method described in reference 18 below. The "Intellectual Curiosity Scale" may be evaluated, for example, based on the method described in reference 19 below. The "Attitude Towards Death Scale (ATDS-A)" may be evaluated, for example, based on the method described in reference 20 below. The "Affective Competence Profile (Japanese Short Version)" may be evaluated, for example, based on the method described in reference 21 below. The "Adolescent / Adult Life Skills Scale" may be evaluated, for example, based on the method described in reference 22 below. The "Self-Control Scale (Short Version)" may be evaluated, for example, based on the method described in reference 23 below. 17. Oshio, A., Abe, S., & Cutrone, P. (2012). Development, reliability, and validity of the Japanese Version of Ten Item Personality Inventory (TIPI-J). The Japanese Journal of Personality, 21(1), 40-52. 18. Aniagai, Y. (1997). A study on the development of trust in adults and elderly individuals. The Japanese Journal of Educational Psychology, 45(1), 79-86. 19. Nishikawa, K., & Amemiya, T. (2015). Development of an Epistemic Curiosity Scale.The Japanese Journal of Educational Psychology, 63(4), 412-425. 20. Tange, C., Nishita, Y., Tomida, M., Otsuka, R., Ando, ​​F., & Shimokata, H. (2016). Longitudinal study of attitudes toward death among middle-aged and elderly Japanese. The Japanese Journal of Developmental Psychology, 27(3), 232-242. 21. Nozaki, Y., & Koyasu, M. (2015). Development of a Japanese version of a short form of the Profile of Emotional Competence. The Japanese Journal of Psychology, 86(2), 160-169. 22. Kase, T., Iimura, S., Bannai, K., & Oishi, K. (2016). Development of the Life Skills Scale for adolescents and adults. The Japanese Journal of Psychology, 87(5), 546-555. 23. Ozaki, Y., Goto, T., Kobayashi, M., & Kutsuzawa, G. (2016). Reliability and validity of the Japanese translation of Brief Self-Control Scale (BSCS-J). The Japanese Journal of Psychology, 87(2), 144-154.

[0068] The information acquisition unit 11 may, for example, acquire the subjective age information and attribute information from the memory 102 or storage device 104 of the device 10, or from other devices other than the device 10.

[0069] The information acquisition unit 11 may, for example, acquire the results of the questionnaire regarding subjective age, and acquire the subjective age information identified from the results of the questionnaire as the subjective age information.

[0070] The information acquisition unit 11 may, for example, acquire the results of a questionnaire regarding demographic data and acquire the attribute information identified from the results of the questionnaire as the attribute information.

[0071] The classification unit 12 classifies the psychological maturity of the subject into a predetermined class based on the psychological evaluation information, the subjective age information, and the attribute information. This classification is performed by a classification model that outputs a predetermined class when the psychological evaluation information, the subjective age information, and the attribute information are input (S12A, classification step).

[0072] The classification model may, for example, be a machine learning-based model. The classification model may, for example, be a trained model generated by machine learning using training data in which the psychological evaluation information, the subjective age information, and the attribute information are explanatory variables and the predetermined class is the dependent variable. The machine learning method may, for example, be gradient boosting. The explanatory variables may be appropriately selected by, for example, a variable selection method. The variable selection method may, for example, be Recursive Feature Elimination (RFE). In this case, the psychological evaluation information may include, for example, psychological evaluation information relating to at least one of mental health and adaptation, psychological evaluation information relating to at least one of self-concept and identity, psychological evaluation information relating to at least one of development and lifespan, psychological evaluation information relating to at least one of personality and personal characteristics, and psychological evaluation information relating to at least one of skills and competence.

[0073] The classification model may be constructed by machine learning using training data in which the psychological evaluation information, subjective age information, and attribute information are used as explanatory variables and a predetermined class relating to psychological maturity is used as the target variable, and the model may be designed to cause a computer to function so that when the psychological evaluation information, subjective age information, and attribute information are input as explanatory variables, the predetermined class relating to psychological maturity is output as the target variable. In this case, the psychological evaluation information may include, for example, psychological evaluation information relating to at least one of mental health and adaptation, psychological evaluation information relating to at least one of self-concept and identity, psychological evaluation information relating to at least one of development and lifespan, psychological evaluation information relating to at least one of personality and personal characteristics, and psychological evaluation information relating to at least one of skills and competence.

[0074] The method for generating the classification model includes, for example, a step of acquiring training data and a step of generating the classification model. Each of the steps in the method for generating the classification model is performed, for example, by a computer. The training data acquisition step acquires, for example, training data in which the psychological evaluation information, the subjective age information, and the attribute information are used as explanatory variables, and a predetermined class relating to psychological maturity is used as the target variable. The classification model generation step generates, for example, a classification model that outputs a predetermined class relating to psychological maturity as the target variable when the psychological evaluation information, the subjective age information, and the attribute information are input as explanatory variables, by machine learning using the training data. In this case, the psychological evaluation information may include, for example, psychological evaluation information relating to at least one of mental health and adaptation, psychological evaluation information relating to at least one of self-concept and identity, psychological evaluation information relating to at least one of development and lifespan, psychological evaluation information relating to at least one of personality and personal characteristics, and psychological evaluation information relating to at least one of skills and competence.

[0075] The classification model may be stored, for example, in the memory 102 or storage device 104 of the device 10, or in another device other than the device 10.

[0076] If the device 10 includes an output unit, it may output the predetermined class. The output unit of the device 10 may output the predetermined class to the output device 106 of the device 10, or to another device other than the device 10.

[0077] The classification program of this disclosure (hereinafter also referred to as the program of this disclosure) is a program that causes a computer to execute each step of the classification method of this disclosure described above. Specifically, the program of this disclosure is a program that causes a computer to execute an information acquisition procedure and a classification procedure. Alternatively, the program of this disclosure can also be described as a program that causes a computer to function as an information acquisition procedure and a classification procedure. In the program of this disclosure, the information acquisition procedure further acquires subjective age information and attribute information of the subject, and the classification procedure classifies the subject's psychological maturity into a predetermined class based on the psychological evaluation information, subjective age information, and attribute information, and the classification is executed by a classification model that outputs a predetermined class when the psychological evaluation information, subjective age information, and attribute information are input. The program of this disclosure can be implemented, for example, using the apparatus 10 shown in Figure 1 or Figure 2. However, the program of this disclosure is not limited to a program that uses the apparatus 10, for example. The program of this disclosure can be implemented by referring to the descriptions of the method and apparatus of this disclosure, for example. Each of the above steps can be interpreted, for example, by substituting "step" with "process."

[0078] As described above, according to the classification device of this disclosure, the information acquisition unit further acquires subjective age information and attribute information of the subject, and the classification unit classifies the subject's psychological maturity into the predetermined class based on the psychological evaluation information, subjective age information, and attribute information. The classification can be performed by a classification model that outputs the predetermined class when the psychological evaluation information, subjective age information, and attribute information are input. Therefore, according to this disclosure, for example, psychological maturity can be classified into predetermined classes with even greater accuracy.

[0079] [Embodiment 3] The classification program of this disclosure may be recorded on, for example, a computer-readable storage medium. The storage medium is, for example, a non-transitory computer-readable storage medium. The storage medium is not particularly limited and includes, for example, random access memory (RAM), read-only memory (ROM), hard disk (HD), flash memory (e.g., SSD (Solid State Drive), USB flash memory, SD / SDHC card, etc.), optical disc (e.g., CD-R / CD-RW, DVD-R / DVD-RW, BD-R / BD-RE, etc.), magneto-optical disk (MO), floppy disk (FD), etc. The classification program of this disclosure (for example, also called a programming product or program product) may be delivered, for example, from an external computer. The "delivery" may be, for example, delivery via a communication network or delivery via a wired connected device. The classification program of this disclosure may be installed and executed on the delivered device, or it may be executed without being installed. An information processing device capable of executing the classification program of this disclosure may be, for example, the classification device of this disclosure.

[0080] The following describes embodiments of this disclosure. However, this disclosure is not limited to the embodiments described below.

[0081] In this embodiment, it is possible to classify the psychological maturity of subjects into six predetermined classes based on a dataset of 79 items, as explained in "1. Classification of Psychological Maturity into Six Predetermined Classes" below. Then, in "2. Generation of Classification Model" below, the generation of the classification model of this disclosure is explained.

[0082] 1. Classification of Psychological Maturity into Six Predetermined Classes (1) Acquisition of a Dataset Consisting of 79 Items The results of a web-based survey targeting test subjects were used as the dataset. The test subjects consisted of 3,000 people who responded to the web-based survey. The mean chronological age ± standard deviation of the test subjects was 45 ± 14 years. The web-based survey questionnaire consisted of a questionnaire on psychological scales (65 items), a questionnaire on subjective age (6 items), and a questionnaire on demographic data (8 items). Based on the results of the web-based survey questionnaire, a dataset of 79 items for the 3,000 test subjects was acquired. The questionnaire on psychological scales was based on 23 evaluation items from Tables 1 to 3 (including 65 detailed evaluation items as dependent items). The questionnaire on subjective age was based on the six subjective age items mentioned above (i.e., perceived age, cognitive age, desired age, ideal age, age of interest, and subjective life expectancy). The subjective age was calculated by subtracting perceived age, cognitive age, desired age, ideal age, or age of interest from chronological age, and by adding subjective life expectancy to chronological age. The questionnaire regarding the demographic data used was based on the eight items related to demographic data mentioned above (i.e., gender, employment status, marital status, presence or absence of children, junior high school graduate status, high school graduate status, university graduate status, and graduate school graduate status). The results of the statistical analysis of the questionnaire regarding subjective age and the questionnaire regarding demographic data are shown in Figure 5.

[0083] (2) Dimensionality Reduction and Cluster Classification The dataset of 79 items for the 3000 subjects was converted into two-dimensional coordinate information by dimensionality reduction using UMAP. Each plot on the graph represented by the two-dimensional coordinate information was classified into six clusters by cluster classification using the Louvain method. The graphs classified into the six clusters are shown in Figure 6(A). The proportion of subjects included in each cluster out of the total subjects was smallest for cluster 1 at 10.5% and largest for cluster 6 at 22.2%, as shown in Figure 6(B). Furthermore, as shown in Figures 7(A) and (B), clusters 1, 2, 5, and 6 mainly included subjects whose chronological age was 40 or older (shown as 40s, 50s, and 60s in Figure 7(A), and as middle age and old age in Figure 7(B)). On the other hand, cluster 3 mainly consisted of subjects whose chronological age was in their 20s or 30s (shown as 20s and 30s in Figure 7(A), and as young adulthood in Figure 7(B)).

[0084] (3) Analysis of the psychological maturity of the six clusters Based on the reference literature of Eryilmaz, A., the psychological maturity of clusters 1 to 6 was evaluated, and it was found that clusters 2 and 5 had a high level of psychological maturity, clusters 1, 3, and 4 had a moderate level of psychological maturity, and cluster 6 had a low level of psychological maturity.

[0085] (4) Analysis of subjective age in the six clusters. The Fold Change (FC) of six items related to subjective age was compared between the clusters. The FC was used as the value obtained by converting each explanatory variable to a base-2 logarithm. The mean ± standard deviation of each subjective age in each cluster is shown in Figure 8.

[0086] The FC (Focus Factor) of Cluster 1 was small in both the age difference regarding ideal age (FC = 1.3) and the age difference regarding age of interest (FC = 1.04). Thus, the subjects included in Cluster 1 showed a tendency to desire being young.

[0087] Cluster 2 showed large differences in FC (Factor Function) between perceived age (FC = 1.61) and cognitive age (FC = 1.59). Furthermore, differences were observed in Cluster 2's FC between ideal age (FC = 1.24) and desired age (FC = 1.21). Similarly, Cluster 4 showed large differences in FC between perceived age (FC = 1.37) and cognitive age (FC = 1.45). Cluster 5 also showed large differences in FC between perceived age (FC = 1.51) and desired age (FC = 1.32). Thus, subjects in Clusters 2, 4, and 5 tended to perceive themselves as younger.

[0088] Cluster 3 showed small chronological age differences in both perceived age (FC = 0.31) and cognitive age (FC = 0.44). Furthermore, subjects in Cluster 3 tended to have perceived ages that matched their chronological age.

[0089] Cluster 6 showed small chronological age differences in both perceived age (FC = 0.36) and cognitive age (FC = 0.54). Furthermore, subjects in Cluster 6 tended to perceive their age as older than their chronological age.

[0090] (5) Analysis of psychological scales in the six clusters Next, the results of comparing the FC of 65 items related to psychological scales between the clusters are shown below. The FC was used as the value obtained by converting each explanatory variable to a base-2 logarithm.

[0091] Cluster 1 showed low FC scores for generational inheritance behaviors (FC = 0.7) and generational inheritance concerns (FC = 0.81). Furthermore, while Cluster 1 had lower levels of depression and anxiety (FC = 0.79) and slightly higher life satisfaction (FC = 0.94), it also showed higher levels of social isolation (FC = 1.07). Thus, participants in Cluster 1 exhibited a mixture of satisfaction and social hesitation.

[0092] Cluster 2 participants showed high FC scores in psychosocial identity (FC = 1.38), other-oriented identity (FC = 1.35), and intrinsic identity (FC = 1.37). Thus, participants in Cluster 2 had high self-awareness and social agreeableness. Furthermore, Cluster 2 participants showed high FC scores in self-consistency and continuity (FC = 1.41), extraversion (FC = 1.22), and self-control (FC = 1.24). This tendency was further emphasized by their high life satisfaction scores in the SWLS (FC = 1.38). Overall, participants in Cluster 2 demonstrated psychological stability.

[0093] Cluster 3 showed high levels of generational inheritance behavior (FC = 1.6), reflecting a need for external evaluation. Cluster 3 also showed high levels of depression and anxiety (FC = 1.68), indicating underlying mental stress despite active intergenerational interaction.

[0094] In Cluster 4, participants showed high levels of generational succession behavior (FC = 1.22) but low levels of coherence (FC = 0.87). Thus, participants in Cluster 4 exhibited a conflict between life satisfaction and self-consistency.

[0095] Cluster 5 showed high FC scores for generational succession behavior (FC = 1.22), will to live out one's life (FC = 1.17), and diffusive curiosity (FC = 1.13).

[0096] Cluster 6 participants showed low levels of life satisfaction (FC = 0.85) and clarity of self-concept (FC = 0.74) in the SWLS, along with high levels of depression and anxiety (FC = 1.86) and poor emotional coping skills (FC = 0.69). Thus, participants in Cluster 6 exhibited psychological and social difficulties. Furthermore, participants in Cluster 6 showed a state diametrically opposed to the psychological stability of participants in Cluster 2.

[0097] Based on the above, it was found that psychological maturity can be classified into six classes based on the 79-item dataset. Furthermore, it was found that each of the six classes has distinctive characteristics in terms of psychological maturity and subjective age.

[0098] 2. Generation of Classification Model (1) Generation of Classification Model Using 79 Items A classification model was designed using gradient boosting that allows for the classification of psychological maturity into one of six classes when 79 explanatory variables are input. As a result, the classification model had an Area Under the Curve (AUC) of 0.96 or higher, and was able to classify psychological maturity with good accuracy (Figure 9(A)). In addition, the classification model using 79 explanatory variables generally had high F1 scores, reflecting its high recall.

[0099] (2) Generation of classification models using 10 or 5 items The explanatory variables were reduced by variable selection using Recursive Feature Elimination (RFE). As a result of reducing the number of explanatory variables to 10 or 5 items through the above variable selection, the AUC of the classification model using 10 explanatory variables was 0.89 or higher, and the AUC of the classification model using 5 explanatory variables was 0.75 or higher, indicating that psychological maturity could still be classified with good accuracy (Figures 9(B) and (C)).

[0100] Furthermore, the essential explanatory variables for accurately classifying psychological maturity were the five items: “composure and maturity,” “generational sense of accomplishment,” “control of behavioral inhibition,” “physical ailments,” and “depression / anxiety (mental health).” These five explanatory variables correspond to psychological assessment information relating to at least one of mental health and adaptation, and to at least one of development and lifespan. In addition, the ten explanatory variables included the five items above, plus “social withdrawal,” “loss,” “flexibility,” “competence / superiority,” and “generational behavior.” These ten explanatory variables correspond to psychological assessment information relating to at least one of mental health and adaptation, at least one of development and lifespan, and at least one of self-concept and identity. The classification model using the ten explanatory variables had an accuracy rate of 0.735 and a KAPPA coefficient of 0.677. The classification model using five explanatory variables had a correct answer rate of 0.649 and a kappa coefficient of 0.572. Furthermore, as shown in Figures 9(B) and (C), the classification models using 10 or 5 explanatory variables still showed high F1 scores in classes 2, 3, 4, and 6.

[0101] Based on the above, it was found that a classification model using five explanatory variables can accurately classify psychological maturity. Furthermore, it was found that a classification model using ten explanatory variables can classify psychological maturity with even greater accuracy. Moreover, it was found that a classification model using more than ten explanatory variables can classify the psychological maturity of subjects with even greater accuracy.

[0102] Although the present disclosure has been described above with reference to embodiments and examples, the present disclosure is not limited to the embodiments and examples described above. Various modifications to the structure and details of the present disclosure can be understood by those skilled in the art within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0103] This application claims priority based on Japanese Patent Application No. 2025-054255, filed on 27 March 2025, and incorporates all of its disclosures herein.

[0104] <Note> Some or all of the above embodiments may be described as follows, but are not limited to the following. <Classification device> (Note 1) A classification device comprising an information acquisition unit and a classification unit, wherein the information acquisition unit acquires psychological evaluation information of a subject, the psychological evaluation information includes psychological evaluation information relating to at least one of mental health and adaptation, and psychological evaluation information relating to at least one of development and lifespan, the classification unit classifies the psychological maturity of the subject into predetermined classes relating to psychological maturity based on the psychological evaluation information, and the classification is performed by a classification model that outputs the predetermined class when the psychological evaluation information is input. (Note 2) The classification device according to Note 1, wherein the psychological evaluation information further includes psychological evaluation information relating to at least one of self-concept and identity. (Note 3) The classification device according to Note 1 or 2, wherein the information acquisition unit further acquires subjective age information and attribute information of the subject, the classification unit classifies the subject's psychological maturity into a predetermined class based on the psychological evaluation information, the subjective age information, and the attribute information, and the classification is performed by a classification model that outputs a predetermined class when the psychological evaluation information, the subjective age information, and the attribute information are input. (Note 4) The classification device according to Note 3, wherein the subjective age information includes at least one of the following: perceived age, cognitive age, desired age, ideal age, age of interest, and subjective life expectancy. (Note 5) The classification device according to Note 3 or 4, wherein the attribute information includes at least one of the following: gender, work history, educational background, marital status, and children status. (Note 6) The classification device according to any one of Notes 3 to 5, wherein the psychological evaluation information further includes psychological evaluation information relating to at least one of personality and personal characteristics, and psychological evaluation information relating to at least one of skills and competence. (Note 7) The classification device according to any one of Notes 1 to 6, wherein the predetermined class includes a class with a high level of psychological maturity, a class with a moderate level of psychological maturity, and a class with a low level of psychological maturity.(Note 8) The classification device described in Note 7, wherein the predetermined class is a class relating to psychological maturity and subjective age. <Classification Method> (Note 9) A classification method comprising an information acquisition step and a classification step, wherein the information acquisition step acquires psychological evaluation information of a subject, the psychological evaluation information includes psychological evaluation information relating to at least one of mental health and adaptation, and psychological evaluation information relating to at least one of development and lifespan, the classification step classifies the subject's psychological maturity into a predetermined class relating to psychological maturity based on the psychological evaluation information, and the classification is performed by a classification model that outputs the predetermined class when the psychological evaluation information is input, wherein each of the steps is performed by a computer. (Note 10) The classification method described in Note 9, wherein the psychological evaluation information further includes psychological evaluation information relating to at least one of self-concept and identity. (Note 11) The classification method according to Note 9 or 10, wherein the information acquisition step further acquires subjective age information and attribute information of the subject, the classification step classifies the subject's psychological maturity into a predetermined class based on the psychological evaluation information, the subjective age information, and the attribute information, and the classification is performed by a classification model that outputs a predetermined class when the psychological evaluation information, the subjective age information, and the attribute information are input. (Note 12) The classification method according to Note 11, wherein the subjective age information includes at least one of the following: perceived age, cognitive age, desired age, ideal age, age of interest, and subjective life expectancy. (Note 13) The classification method according to Note 11 or 12, wherein the attribute information includes at least one of the following: gender, work history, educational background, marital status, and children status. (Note 14) The classification method described in any of Notes 11 to 13, wherein the aforementioned psychological evaluation information further includes psychological evaluation information relating to at least one of personality and personal characteristics, and psychological evaluation information relating to at least one of skills and competencies.(Note 15) The classification method described in any of Notes 9 to 14, wherein the predetermined class includes a class with a high level of psychological maturity, a class with a moderate level of psychological maturity, and a class with a low level of psychological maturity. (Note 16) The classification method described in Note 15, wherein the predetermined class is a class relating to psychological maturity and subjective age. <Classification Program> (Note 17) A classification program for causing a computer to execute each of the above procedures, comprising: an information acquisition procedure and a classification procedure, wherein the information acquisition procedure acquires psychological evaluation information of a subject, the psychological evaluation information includes psychological evaluation information relating to at least one of mental health and adaptation, and psychological evaluation information relating to at least one of development and lifespan, the classification procedure classifies the subject's psychological maturity into a predetermined class relating to psychological maturity based on the psychological evaluation information, and the classification is performed by a classification model that outputs the predetermined class when the psychological evaluation information is input. (Note 18) The classification program described in Note 17, wherein the psychological evaluation information further includes psychological evaluation information relating to at least one of self-concept and identity. (Note 19) The classification program described in Note 17 or 18, wherein the information acquisition procedure further acquires subjective age information and attribute information of the subject, the classification procedure classifies the subject's psychological maturity into a predetermined class based on the psychological evaluation information, the subjective age information, and the attribute information, and the classification is performed by a classification model that outputs a predetermined class when the psychological evaluation information, the subjective age information, and the attribute information are input. (Note 20) The classification program described in Note 19, wherein the subjective age information includes at least one of the following: perceived age, cognitive age, desired age, ideal age, age of interest, and subjective life expectancy. (Note 21) The classification program described in Note 19 or 20, wherein the attribute information includes at least one of the following: gender, work history, educational background, marital status, and children status.(Note 22) The classification program according to any one of Notes 19 to 21, wherein the psychological evaluation information further includes psychological evaluation information relating to at least one of personality and personal characteristics, and psychological evaluation information relating to at least one of skills and competence. (Note 23) The classification program according to any one of Notes 17 to 22, wherein the predetermined class includes a class with a high level of psychological maturity, a class with a moderate level of psychological maturity, and a class with a low level of psychological maturity. (Note 24) The classification program according to Note 23, wherein the predetermined class is a class relating to psychological maturity and subjective age. <Recording medium> (Note 25) A computer-readable recording medium that records a program for causing a computer to execute each of the above procedures, including an information acquisition procedure and a classification procedure, wherein the information acquisition procedure acquires psychological evaluation information of a subject, the psychological evaluation information includes psychological evaluation information relating to at least one of mental health and adaptation, and psychological evaluation information relating to at least one of development and lifespan, the classification procedure classifies the subject's psychological maturity into a predetermined class relating to psychological maturity based on the psychological evaluation information, and the classification is performed by a classification model that outputs the predetermined class when the psychological evaluation information is input. (Note 26) The recording medium according to Note 25, wherein the psychological evaluation information further includes psychological evaluation information relating to at least one of self-concept and identity. (Note 27) The recording medium according to Note 25 or 26, wherein the information acquisition procedure further acquires subjective age information and attribute information of the subject, the classification procedure classifies the subject's psychological maturity into a predetermined class based on the psychological evaluation information, the subjective age information, and the attribute information, and the classification is performed by a classification model that outputs a predetermined class when the psychological evaluation information, the subjective age information, and the attribute information are input. (Note 28) The recording medium according to Note 27, wherein the subjective age information includes at least one of the following: perceived age, cognitive age, desired age, ideal age, age of interest, and subjective life expectancy.(Note 29) The recording medium according to Note 27 or 28, wherein the attribute information includes at least one of the following: information on gender, information on work history, information on educational background, information on marital status, and information on whether or not the person has children. (Note 30) The recording medium according to any one of Notes 27 to 29, wherein the psychological evaluation information further includes psychological evaluation information relating to at least one of personality and personal characteristics, and psychological evaluation information relating to at least one of skills and competence. (Note 31) The recording medium according to any one of Notes 25 to 30, wherein the predetermined class includes a class with a high level of psychological maturity, a class with a medium level of psychological maturity, and a class with a low level of psychological maturity. (Note 32) The recording medium according to Note 31, wherein the predetermined class is a class relating to psychological maturity and subjective age. <Classification Model> (Note 33) A classification model constructed by machine learning using training data with psychological evaluation information as explanatory variables and a predetermined class relating to psychological maturity as the dependent variable, wherein the psychological evaluation information includes psychological evaluation information relating to at least one of mental health and adaptation, and psychological evaluation information relating to at least one of development and lifespan, and which causes a computer to function so that when the psychological evaluation information is input as an explanatory variable, it outputs the predetermined class relating to psychological maturity as the dependent variable. <Classification Model Generation Method> (Appendix 34) A classification model generation method comprising a learning data acquisition step and a classification model generation step, wherein the learning data acquisition step acquires learning data in which psychological evaluation information is used as an explanatory variable and a predetermined class relating to psychological maturity is used as the objective variable, the psychological evaluation information includes psychological evaluation information relating to at least one of mental health and adaptation, and psychological evaluation information relating to at least one of development and lifespan, and the classification model generation step generates a classification model by machine learning using the learning data that outputs a predetermined class relating to psychological maturity as the objective variable when the psychological evaluation information is input as an explanatory variable, each of the steps being performed by a computer.<Classification Model> (Appendix 35) A classification model constructed by machine learning using training data with psychological evaluation information, subjective age information, and attribute information as explanatory variables, and a predetermined class relating to psychological maturity as the dependent variable, wherein the psychological evaluation information includes psychological evaluation information relating to at least one of mental health and adaptation, psychological evaluation information relating to at least one of development and lifespan, psychological evaluation information relating to at least one of self-concept and identity, psychological evaluation information relating to at least one of personality and personal characteristics, and psychological evaluation information relating to at least one of skills and competence, and which causes a computer to function so that when the psychological evaluation information, subjective age information, and attribute information are input as explanatory variables, the predetermined class relating to psychological maturity is output as the dependent variable. <Classification Model Generation Method> (Appendix 36) A classification model generation method comprising a learning data acquisition step and a classification model generation step, wherein the learning data acquisition step acquires learning data in which psychological evaluation information, subjective age information, and attribute information are used as explanatory variables and a predetermined class relating to psychological maturity is used as the objective variable, the psychological evaluation information includes psychological evaluation information relating to at least one of mental health and adaptation, psychological evaluation information relating to at least one of development and lifespan, psychological evaluation information relating to at least one of self-concept and identity, psychological evaluation information relating to at least one of personality and personal characteristics, and psychological evaluation information relating to at least one of skills and competence, and the classification model generation step generates a classification model by machine learning using the learning data that outputs a predetermined class relating to psychological maturity as the objective variable when the psychological evaluation information, subjective age information, and attribute information are input as explanatory variables, and each of the above steps is performed by a computer.

[0105] According to this disclosure, psychological maturity can be classified into predetermined classes. Therefore, this disclosure can be widely and usefully used in various fields, including psychology.

[0106] 10 Classification device 11 Information acquisition unit 12 Classification unit 101 Central processing unit 102 Memory 103 Bus 104 Storage device 105 Input device 106 Output device 107 Communication device

Claims

1. A classification device comprising an information acquisition unit and a classification unit, wherein the information acquisition unit acquires psychological evaluation information of a subject, the psychological evaluation information includes psychological evaluation information relating to at least one of mental health and adaptation, and psychological evaluation information relating to at least one of development and lifespan, the classification unit classifies the subject's psychological maturity into predetermined classes relating to psychological maturity based on the psychological evaluation information, and the classification is performed by a classification model that outputs the predetermined class when the psychological evaluation information is input.

2. The classification device according to claim 1, wherein the psychological evaluation information further includes psychological evaluation information relating to at least one of self-concept and identity.

3. The classification device according to claim 1, wherein the information acquisition unit further acquires subjective age information and attribute information of the subject, the classification unit classifies the subject's psychological maturity into a predetermined class based on the psychological evaluation information, the subjective age information, and the attribute information, and the classification is performed by a classification model that outputs a predetermined class when the psychological evaluation information, the subjective age information, and the attribute information are input.

4. The classification device according to claim 3, wherein the subjective age information includes at least one of the following: information relating to perceived age, information relating to cognitive age, information relating to desired age, information relating to ideal age, information relating to age of interest, and information relating to subjective life expectancy.

5. The classification device according to claim 3, wherein the attribute information includes at least one of the following: information on gender, information on work history, information on educational background, information on marital status, and information on whether or not a person has children.

6. The classification device according to claim 3, wherein the psychological evaluation information further includes psychological evaluation information relating to at least one of personality and personal characteristics, and psychological evaluation information relating to at least one of skills and competence.

7. The classification device according to any one of claims 1 to 6, wherein the predetermined class includes a class with a high level of psychological maturity, a class with a moderate level of psychological maturity, and a class with a low level of psychological maturity.

8. The classification device according to claim 7, wherein the predetermined class is a class relating to psychological maturity and subjective age.

9. A classification method comprising an information acquisition step and a classification step, wherein the information acquisition step acquires psychological evaluation information of a subject, the psychological evaluation information includes psychological evaluation information relating to at least one of mental health and adaptation, and psychological evaluation information relating to at least one of development and lifespan, the classification step classifies the subject's psychological maturity into predetermined classes relating to psychological maturity based on the psychological evaluation information, and the classification is performed by a classification model that outputs the predetermined classes when the psychological evaluation information is input, and each of the steps is performed by a computer.

10. The classification method according to claim 9, wherein the psychological evaluation information further includes psychological evaluation information relating to at least one of self-concept and identity.

11. The classification method according to claim 9, wherein the information acquisition step further acquires subjective age information and attribute information of the subject, the classification step classifies the subject's psychological maturity into a predetermined class based on the psychological evaluation information, the subjective age information, and the attribute information, and the classification is performed by a classification model that outputs a predetermined class when the psychological evaluation information, the subjective age information, and the attribute information are input.

12. The classification method according to claim 11, wherein the subjective age information includes at least one of the following: information relating to perceived age, information relating to cognitive age, information relating to desired age, information relating to ideal age, information relating to age of interest, and information relating to subjective life expectancy.

13. The classification method according to claim 11, wherein the attribute information includes at least one of the following: information on gender, information on work history, information on educational background, information on marital status, and information on whether or not a person has children.

14. The classification method according to claim 11, wherein the psychological evaluation information further includes psychological evaluation information relating to at least one of personality and personal characteristics, and psychological evaluation information relating to at least one of skills and competence.

15. The classification method according to any one of claims 9 to 14, wherein the predetermined class includes a class with a high level of psychological maturity, a class with a moderate level of psychological maturity, and a class with a low level of psychological maturity.

16. The classification method according to claim 15, wherein the predetermined class is a class relating to psychological maturity and subjective age.

17. A classification program for causing a computer to perform each of the above procedures, including an information acquisition procedure and a classification procedure, wherein the information acquisition procedure acquires psychological evaluation information of a subject, the psychological evaluation information includes psychological evaluation information relating to at least one of mental health and adaptation, and psychological evaluation information relating to at least one of development and lifespan, the classification procedure classifies the subject's psychological maturity into a predetermined class relating to psychological maturity based on the psychological evaluation information, and the classification is performed by a classification model that outputs the predetermined class when the psychological evaluation information is input.

18. The classification program according to claim 17, wherein the psychological evaluation information further includes psychological evaluation information relating to at least one of self-concept and identity.

19. The classification program according to claim 17, wherein the information acquisition procedure further acquires subjective age information and attribute information of the subject, the classification procedure classifies the subject's psychological maturity into a predetermined class based on the psychological evaluation information, the subjective age information, and the attribute information, and the classification is performed by a classification model that outputs a predetermined class when the psychological evaluation information, the subjective age information, and the attribute information are input.

20. A computer-readable recording medium that records a program for causing a computer to execute each of the above procedures, including an information acquisition procedure and a classification procedure, wherein the information acquisition procedure acquires psychological evaluation information of a subject, the psychological evaluation information includes psychological evaluation information relating to at least one of mental health and adaptation, and psychological evaluation information relating to at least one of development and lifespan, the classification procedure classifies the subject's psychological maturity into a predetermined class relating to psychological maturity based on the psychological evaluation information, and the classification is performed by a classification model that outputs the predetermined class when the psychological evaluation information is input.

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