Sensory treatment evaluation method and device for children of 3-6 years old and storage medium

Abstract

The invention relates to a feeling processing evaluation method and device for children of 3-6 years old and a storage medium, and is applied to the technical field of child feeling processing ability evaluation.The method comprises the steps that existing feeling processing related items are screened to obtain a theoretical evaluation scale; acquiring scores of the target test population for the theoretical evaluation scale, performing question quality inspection and screening on the theoretical evaluation scale based on the questionnaire scores, and obtaining a scale factor model for expressing social behaviors of children and a feeling processing model for expressing feeling processing ability based on the screened questions, standard score conversion is carried out through the original score ranking of the screened questions in the test crowd, so that a standardized norm is obtained; standard scores of a to-be-tested child about all questions are obtained, and then scores of the to-be-tested child in all dimensions of social behaviors and feeling processing ability are obtained and used for quantitatively evaluating the feeling processing function of the to-be-tested child. And the problem of relatively low accuracy of an evaluation result caused by subjective experience judgment of the feeling processing ability of the child is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of children's sensory processing ability assessment, and particularly relates to a sensory processing assessment method, device and storage medium for children aged 3 - 6 years old. Background Art

[0002] When monitoring and evaluating children's sensory processing functions, using standardized assessment tools can provide objective data on various aspects of the measured children in this field. As a quantifiable measurement tool, the rating scale reflects the behaviors of the measured subjects and the impressions obtained from observing them, and is one of the important means for collecting data in developmental behavior psychology assessment. It cooperates with the subjective experience and clinical observations of the assessment implementers to form a more scientific assessment system from multiple perspectives. However, the existing assessment rating scales are all foreign scales.

[0003] Currently in our country, either imported foreign rating scales are used for assessment and measurement, which leads to low accuracy of the assessment results due to their inapplicability to the situation of children in our country, or simply the sensory processing ability of children is judged through experience, which also results in low accuracy of the assessment results due to subjectivity and limitations. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a sensory processing assessment method, device and storage medium for children aged 3 - 6 years old, so as to solve the problems in the prior art that the imported rating scale assessment tools are not applicable to the situation of local children, and the accuracy of the assessment results caused by judging children's sensory processing ability through subjective experience is relatively low.

[0005] According to the first aspect of the embodiments of the present invention, a sensory processing assessment method for children aged 3 - 6 years old is provided, and the method includes:

[0006] Generating an initial assessment rating scale according to the relevant items of the existing sensory processing theory, and screening the initial assessment rating scale according to a preset screening criterion to obtain a theoretical assessment rating scale;

[0007] Obtaining the scores of each item in the theoretical assessment rating scale for the target test population and the credibility scores for each item;

[0008] Adding up the scores of the target test population for the same item, obtaining the total score of each item in the theoretical assessment rating scale, and respectively obtaining the Kaiser - Meyer - Olkin (KMO) value of the data quality test and the Bartlett's test of sphericity P - value according to the total score of each item; at the same time, obtaining the factor loading coefficients of each item with other items, and deleting the item with a smaller factor loading coefficient so that the remaining items simultaneously meet the KMO standard value and the Bartlett's test of sphericity standard value;

[0009] Items with factor load coefficients greater than a preset factor load threshold among the remaining items are classified into the same group through the varimax rotation method, with each group being a factor. Factors that do not meet the preset conditions are deleted to obtain multiple target factors. Each target factor is named according to the social behavior dimension to obtain a scale factor model composed of each factor;

[0010] All items included in the scale factor model are respectively classified under a preset number of sensory processing dimensions to obtain a sensory processing model;

[0011] Judgment of the goodness-of-fit index of the sensory processing model is carried out. By deleting the item with the least factor load coefficient, multiple goodness-of-fit indexes of the sensory processing model all meet the preset index criteria, and the corresponding items in the scale factor model are synchronously deleted so that the items included in the scale factor model are consistent with those in the sensory processing model;

[0012] Cronbach coefficient judgment is carried out on the scale factor model and the sensory processing model according to the credibility scores of each item by the target test population. By deleting the item with the least factor load coefficient, the sensory processing model and the scale factor model meet the preset coefficient standard value;

[0013] Cross-correlation verification is carried out on the scale factor model and the sensory processing model. By deleting the item with the least factor load coefficient, the sensory processing model and the scale factor model meet the preset cross-correlation threshold;

[0014] Each remaining item in the scale factor model or the sensory processing model is sorted according to the scores of the target test population for normal distribution transformation into standard scores, and a standardized norm is formed according to the distribution of the standard scores;

[0015] The original scores of each item in the scale factor model or the sensory processing model are obtained from the parents of children aged 3 - 6 to be tested, and the standard scores of each item are obtained according to the standardized norm. The social behavior dimension scores and multiple sensory processing dimension scores are obtained according to the standard scores of each item.

[0016] Preferably,

[0017] The initial assessment scale is screened according to the preset screening criteria, and the theoretical assessment scale obtained includes:

[0018] Among all the items of the initial assessment scale, relevant items whose measurement assessment age range is not in the 3 - 6-year-old interval are deleted;

[0019] Among all the items of the initial assessment scale, relevant items containing the specified keyword are deleted; the specified keyword is the symptom keyword of the specified disease.

[0020] Preferably,

[0021] The target test population includes:

[0022] The target test population is N parents of children aged 3 - 6 in all target cities;

[0023] The male - female ratio and the urban - rural population ratio of the N parents meet the preset ratio standards;

[0024] The children aged 3 - 6 corresponding to the N parents are not children with designated diseases, and the children aged 3 - 6 corresponding to the N parents do not attend special children's schools.

[0025] Preferably,

[0026] The deletion of factors that do not meet the preset conditions to obtain multiple target factors includes:

[0027] Obtain the number of items corresponding to each factor, and delete the items under the factor with the number of items less than the preset number of items;

[0028] Obtain the eigenvalue of each factor, and delete the items under the factor whose eigenvalue does not meet the preset eigenvalue;

[0029] Recalculate the factor loading coefficient of each item with other items for the remaining items after deleting the items, and classify the items with the factor loading coefficient greater than the preset factor loading threshold into the same group through the varimax rotation method to obtain multiple new factors. The number of factors in each new classification is less than the number of factors in the previous classification. Continue to judge whether the number of items corresponding to each factor and the eigenvalue meet the preset number of items and the preset eigenvalue. If not, continue to delete the items under the corresponding factor, and repeat to generate new factors until the number of items corresponding to each generated factor and the eigenvalue meet the preset number of items and the preset eigenvalue. The remaining factors are used as target factors.

[0030] Preferably,

[0031] The judgment of the fitting index of the sensory processing model, and making multiple fitting indexes of the sensory processing model meet the preset index standards by deleting the item with the least factor loading coefficient includes:

[0032] Obtain multiple fitting indexes of the sensory processing model, compare each fitting index with the corresponding index standard respectively. If any one of the fitting indexes does not meet the preset index standard, delete one item with the smallest factor loading coefficient in the sensory processing model until multiple fitting indexes of the sensory processing model meet the preset index standards;

[0033] The multiple fitting indexes include chi-square over degrees of freedom, goodness-of-fit index GFI, root mean square error of approximation RMSEA, root mean square residual RMR, comparative fit index CFI, normed fit index NFI, and non-normed fit index NNFI.

[0034] Preferably,

[0035] Performing Cronbach coefficient judgment on the scale factor model and the sensory processing model according to the credibility scores of each item for the target test population, and making the sensory processing model and the scale factor model meet the preset coefficient standard value by deleting the item with the least factor loading coefficient, including:

[0036] Obtaining the total credibility score of the target test population for each item in the scale factor model or the sensory processing model, obtaining the total credibility score of the target test population for all items based on the total credibility score of the target test population for each item, obtaining the Cronbach coefficient based on the total credibility score of the target test population for all items. If the Cronbach coefficient does not meet the preset coefficient standard value, then synchronously delete the item with the smallest factor loading coefficient in both the scale factor model and the sensory processing model, re-obtain the Cronbach coefficient, continue to judge whether the Cronbach coefficient meets the preset coefficient standard value, and repeat the item deletion process until the Cronbach coefficient meets the preset coefficient standard value.

[0037] Preferably,

[0038] Performing cross-correlation verification on the scale factor model and the sensory processing model, and making the sensory processing model and the scale factor model meet the preset cross-correlation threshold by deleting the item with the least factor loading coefficient, including:

[0039] Obtaining the correlation coefficient between any factor in the scale factor model and any dimension in the sensory processing model. If the correlation coefficient does not meet the preset correlation coefficient threshold, then synchronously delete the item with the smallest factor loading coefficient in both the scale factor model and the sensory processing model, re-obtain the correlation coefficient, continue to judge whether the correlation coefficient meets the preset correlation coefficient threshold, and repeat the item deletion process until the correlation coefficient meets the preset correlation coefficient threshold.

[0040] According to the second aspect of the embodiments of the present invention, there is provided a sensory processing evaluation device for children aged 3 - 6, and the device includes:

[0041] An initial scale generation module: used to generate an initial evaluation scale according to the existing items related to sensory processing theory, and screen the initial evaluation scale according to a preset screening criterion to obtain a theoretical evaluation scale;

[0042] Questionnaire score collection module: used to obtain the scores of each item in the theoretical evaluation scale for the target test population and the credibility scores for each item;

[0043] KMO and Bartlett spherical test module: used to add up the scores of the target test population for the same item, obtain the total score of each item in the theoretical evaluation scale, and respectively obtain the KMO value of the data quality test and the Bartlett spherical test P value according to the total score of each item; at the same time, obtain the factor loading coefficients of each item with other items, and make the remaining items meet the KMO standard value and the Bartlett spherical test standard value by deleting the item with a smaller factor loading coefficient;

[0044] Scale factor module: used to classify the items with factor loading coefficients greater than the preset factor loading threshold in the remaining items into the same group, each group is a factor, delete the factors that do not meet the preset conditions to obtain multiple target factors, and name each target factor according to the social behavior dimension to obtain a scale factor model composed of each factor;

[0045] Sensory processing model generation module: used to classify all the items included in the scale factor model into multiple preset sensory processing dimensions respectively to obtain a sensory processing model;

[0046] Fitting index screening module: used to judge the fitting index of the sensory processing model, and make multiple fitting indexes of the sensory processing model meet the preset index standards by deleting the item with the least factor loading coefficient, and synchronously delete the corresponding item in the scale factor model to make the items included in the scale factor model consistent with the sensory processing model;

[0047] Cronbach coefficient screening module: used to judge the Cronbach coefficient of the scale factor model and the sensory processing model according to the credibility scores of the target test population for each item, and make the sensory processing model and the scale factor model meet the preset coefficient standard value by deleting the item with the least factor loading coefficient;

[0048] Correlation coefficient screening module: used to conduct cross-correlation verification on the scale factor model and the sensory processing model, and make the sensory processing model and the scale factor model meet the preset cross-correlation threshold by deleting the item with the least factor loading coefficient;

[0049] Standard norm conversion module: used to sort and convert the remaining items in the scale factor model or the sensory processing model into standard scores according to the scores of the target test population for normal distribution, and form a standardized norm according to the distribution of the standard scores;

[0050] Target test score module: It is used to obtain the original scores of each item in the scale factor model or the sensory processing model from the parents of children aged 3 to 6 to be tested, obtain the standard scores of each item according to the standardized norm, and obtain the social behavior dimension scores and multiple sensory processing dimension scores according to the standard scores of each item.

[0051] According to the third aspect of the embodiments of the present invention, a storage medium is provided. The storage medium stores a computer program, and when the computer program is executed by a main controller, each step in the above method is implemented.

[0052] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0053] In this application, an initial assessment scale is generated through existing items related to sensory processing theory. By screening the initial assessment scale, a theoretical assessment scale is obtained; the scores of the target test population for the theoretical assessment scale are collected, that is, a large data sample of children is obtained. Based on the questionnaire scores, the item quality of the theoretical assessment scale is inspected and screened. Based on the screened items, a scale factor model expressing children's social behavior and a sensory processing model expressing sensory processing ability are obtained. Through the original score ranking of the items in the test stage, standard score conversion is performed, thereby obtaining a standardized norm; by obtaining the standard scores of the to-be-tested children for each item, the scores of the to-be-tested children in each dimension of social behavior and sensory processing ability are obtained, which is used to quantitatively evaluate the sensory processing function of the to-be-tested children; it avoids the situation that the scale assessment tools introduced from abroad are not applicable to local children, and the problem of low accuracy of the assessment results caused by subjectively judging children's sensory processing ability through subjective experience.

[0054] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The drawings here are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention.

[0056] Figure 1 It is a schematic flowchart of a method for assessing sensory processing of children aged 3 to 6 shown according to an exemplary embodiment;

[0057] Figure 2 It is a schematic diagram of an exploratory factor analysis model shown according to another exemplary embodiment;

[0058] Figure 3 It is a schematic diagram of model fitting indexes obtained by exploratory factor analysis shown according to another exemplary embodiment;

[0059] Figure 4It is a schematic diagram of a sensory processing model shown according to another exemplary embodiment;

[0060] Figure 5 It is a schematic diagram of model fitting indexes with the sensory system as a factor structure shown according to another exemplary embodiment;

[0061] Figure 6 It is a schematic diagram of Cronbach reliability analysis shown according to another exemplary embodiment;

[0062] Figure 7 It is a schematic diagram of cross - correlation verification shown according to another exemplary embodiment;

[0063] Figure 8 It is a system schematic diagram of a sensory processing evaluation device for children aged 3 - 6 shown according to another exemplary embodiment;

[0064] In the drawings: 1 - initial scale generation module, 2 - questionnaire score collection module, 3 - KMO and Bartlett's sphericity test module, 4 - scale factor module, 5 - sensory processing model generation module, 6 - fitting index screening module, 7 - Cronbach coefficient screening module, 8 - correlation coefficient screening module, 9 - standard norm conversion module, 10 - target test score module. Detailed implementation manners

[0065] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present invention. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0066] Embodiment 1

[0067] Figure 1 It is a flowchart schematic diagram of a sensory processing evaluation method for children aged 3 - 6 shown according to an exemplary embodiment, as Figure 1 shown, the method includes:

[0068] S1. Generate an initial evaluation scale according to the relevant items of the existing sensory processing theory, and screen the initial evaluation scale according to a preset screening criterion to obtain a theoretical evaluation scale;

[0069] S2. Obtain the scores of each item in the theoretical evaluation scale for the target test population and the credibility scores for each item;

[0070] S3. Add up the scores of the target test population for the same item to obtain the total score of each item in the theoretical evaluation scale. Then, respectively obtain the Kaiser-Meyer-Olkin (KMO) value for data quality inspection and the Bartlett's test of sphericity P-value based on the total score of each item. At the same time, obtain the factor loading coefficients of each item with other items, and make the remaining items meet the KMO standard value and the Bartlett's test of sphericity standard value by deleting the item with a smaller factor loading coefficient.

[0071] S4. Classify the items with factor loading coefficients greater than the preset factor loading threshold among the remaining items into the same group by the varimax rotation method. Each group is a factor. Delete the factors that do not meet the preset conditions to obtain multiple target factors, and name each target factor according to the social behavior dimension to obtain a scale factor model composed of each factor.

[0072] S5. Classify all the items included in the scale factor model into the preset multiple sensory processing dimensions respectively to obtain a sensory processing model.

[0073] S6. Judge the goodness-of-fit index of the sensory processing model. Make the multiple goodness-of-fit indexes of the sensory processing model meet the preset index standards by deleting the item with the least factor loading coefficient, and synchronously delete the corresponding item in the scale factor model to make the items included in the scale factor model consistent with those in the sensory processing model.

[0074] S7. Judge the Cronbach's coefficient of the scale factor model and the sensory processing model according to the credibility scores of the target test population for each item. Make the sensory processing model and the scale factor model meet the preset coefficient standard value by deleting the item with the least factor loading coefficient.

[0075] S8. Conduct cross-correlation verification on the scale factor model and the sensory processing model. Make the sensory processing model and the scale factor model meet the preset cross-correlation threshold by deleting the item with the least factor loading coefficient.

[0076] S9. Sort the remaining items in the scale factor model or the sensory processing model according to the scores of the target test population and transform them into standard scores by normal distribution. Form a standardized norm according to the distribution of the standard scores.

[0077] S10. Obtain the original scores of the parents of children aged 3 - 6 to be tested for each item in the scale factor model or the sensory processing model, and obtain the standard scores of each item according to the standardized norm. Obtain the social behavior dimension scores and multiple sensory processing dimension scores according to the standard scores of each item.

[0078] It is understandable that in this embodiment, first, the structures and related items regarding children's development and sensory processing in various existing domestic and foreign scales are sorted out. In combination with multiple senior occupational therapists for sensory integration training, and after collecting their experiences and opinions on common symptoms of children's sensory processing, more than 100 items of the initial assessment scale are proposed according to their descriptions. Then, according to the milestones of children's ability development, the behavioral characteristic items that do not meet the target age range of this study are modified or deleted (that is, the items that do not meet the abilities of children aged 3 - 6 are deleted), and 74 items are retained. At the same time, the items containing the keywords of the corresponding symptoms of the specified diseases are deleted. The specified diseases include: autism spectrum, Down syndrome, and those who have suffered from nervous system trauma. Finally, 66 items are retained to obtain the theoretical assessment scale;

[0079] In this embodiment, 1,992 parents of children aged 3 - 6 in Beijing, Shanghai, Guangzhou, Shenyang, Xi'an, and Chengdu were randomly selected from 1.2 million samples on a certain comprehensive research platform to participate in this test (that is, the above-mentioned target test population). The sample distribution is basically balanced, with 56% males and 44% females. The sample of each city is in direct proportion to the non - agricultural population in each place. All samples did not deliberately exclude children with behavioral difficulties or deviations in daily life and study, but children who have been diagnosed with autism spectrum, Down syndrome, those who have suffered from nervous system trauma, and those who are studying in special schools are not included in the research samples. In this study, questionnaires were distributed to randomly selected interviewees who met the conditions, and questionnaire data were collected;

[0080] The target test population will score their children according to the requirements of the experiment when answering the questionnaire, so as to obtain the score of each question. In this embodiment, the cumulative total score of each question is added up, and the simple correlation coefficient and partial correlation coefficient between the variables are compared, and the standard KMO for data quality inspection is generated. The KMO value (Kaiser-Meyer-Olkin) is a statistic used to evaluate whether the data is suitable for factor analysis or principal component analysis. Its value range is between 0 and 1. The closer to 1, the higher the correlation between the variables, which is suitable for factor analysis or principal component analysis; the closer to 0, the lower the correlation between the variables, which is not suitable for these analyses. By establishing a correlation matrix, and then calculating the partial correlation coefficient and simple correlation coefficient between the two variables, the KMO value is obtained based on the partial correlation coefficient and the simple correlation coefficient, and the Bartlett's Test of Sphericity (Bartlett's Test of Sphericity is a statistical test method, which is mainly used to test whether the correlation matrix is ​​a unit matrix, that is, to test whether each variable is independent. Before conducting factor analysis, it is usually necessary to conduct a Bartley sphericity test to determine whether the data is suitable for factor analysis. The specific steps of the Bartley sphericity test are as follows: Calculate the correlation coefficient matrix: First, calculate the correlation coefficients between all variables to form a correlation coefficient matrix; Construct statistics: Use the determinant of the correlation coefficient matrix to construct a statistic; Compare statistics: Compare the statistic with the preset significance level; Judge the result: If the statistic is large and the accompanying probability is less than the significance level, reject the null hypothesis, and believe that there is a correlation between the variables, which is suitable for factor analysis; In short, the KMO value and the Bartley sphericity test test the suitability of the data from different angles. The KMO value test mainly checks the partial correlation of the data, while the Bartley sphericity test mainly checks the partial correlation of the data. The Terry sphericity test tests the independence between data; therefore, after obtaining the KMO value and the Bartley sphericity test P value of the data in this application, the KMO value test standard needs to be greater than 0.6, and the Bartley sphericity test P value should be less than 0.05. If any of the above two indicators does not meet the standard, it is necessary to delete the items with lower factor loading coefficients. When the items with lower factor loading coefficients are deleted, the question structure of the scale will be more reasonable, and the above two indicators will also be improved. The next step of factor analysis can only be carried out until the data reaches this standard; and the statistical significance of the factor loading coefficient is the correlation coefficient between the i-th variable and the j-th common factor, that is, the weight (proportion) of variable i's dependence on variable j, which is called weight in statistical terms, and psychologists call it load, that is, the load of the i-th variable on the j-th common factor, which reflects the relative importance of the i-th variable on the j-th common factor;

[0081] Exploratory factor analysis: Figure 2As shown, according to the factor loading coefficients of each item, the items with relatively high correlations (usually with factor loading coefficients greater than 0.4) are grouped together by the varimax-rotation method. Each group is called a factor. Among them, the varimax-rotation method is a transformation method in factor analysis to obtain the rotated factor loading matrix according to the simple structure criterion based on the initial factor loading matrix obtained by the principal axis method, so that the variance of the squared elements in each column of the transformed factor loading matrix is maximized while keeping each element independent; since a relatively large number of factors are formed, it is necessary to delete the factors with less than 3 items under each factor, and at the same time obtain the characteristic roots of each factor, and delete the items under the factors with characteristic roots less than 1; since the factor loading coefficients of each item will also change accordingly after the items are deleted, it is necessary to recalculate the factor loading coefficients of each item and divide them into multiple new factors, and check the number of items and the characteristic root values of each factor again until the characteristic roots of each newly generated factor are greater than 1 and the number of items under each factor is greater than 3. Finally, the group of factors is named according to the dimensions of children's social behavior commonly reflected by the items within each group of retained factors, as shown in Appendix Figure 2 or Appendix Figure 6 As shown, the number of factors finally screened in this embodiment is 5, which represents 5 dimensions of children's social behavior and are respectively named: sensation seeking, sensory discrimination, over-responsiveness, motor planning, and social participation. Thus, a new scale factor model composed of each factor is generated through exploratory factor analysis;

[0082] After obtaining the scale factor model, assuming that the scale factor model contains a total of 44 items, it is necessary to classify the 44 items into 6 sensory processing dimensions to obtain a sensory processing model, as shown in Appendix Figure 4As shown, the six sensory processing dimensions are: proprioception, touch, vestibular sense, vision, hearing, and sensory integration; Next, it is necessary to obtain the chi-square to degrees of freedom ratio, goodness of fit index GFI, root mean square error of approximation RMSEA, root mean square residual RMR, comparative fit index CFI, normed fit index NFI, and non-normed fit index NNFI of the sensory processing model respectively; Among them, the chi-square to degrees of freedom ratio is an important indicator to measure the goodness of fit of the model. Its calculation formula is the chi-square value divided by the degrees of freedom value, usually expressed as CMIN / DF or χ2 / df. The smaller this ratio, the better the fit of the model to the actual situation; conversely, the larger the ratio, the worse the fit; The goodness of fit index (GFI) = 1 - (preset model chi-square value / independent model chi-square value). GFI ranges from 0 to 1, but sometimes meaningless negative values may also occur. The larger the sample size, the larger the GFI; The closer the GFI value is to 1, the higher the goodness of fit of the model; conversely, it indicates a lower goodness of fit of the model; The root mean square error of approximation is an indicator to evaluate the goodness of fit of the model. In structural equation modeling, when trying to fit a theoretical model to actual data, errors may occur. These errors come from the setting of the model itself, the characteristics of the sample data, and various other factors. RMSEA is an indicator used to quantify this kind of error. The higher its value, the worse the fit between the model and the data; conversely, the lower the value, the better the fit; RMSEA is obtained by calculating the residuals between the observed data and the model predicted values, and then taking the root mean square of these residuals. This process takes into account the errors of all variables and provides an overall model fit evaluation; The root mean square residual (RMR) is used to reflect the overall residual of the theoretical hypothesis model. The calculation steps of RMR are as follows: sample covariance matrix - model reproduced covariance matrix = covariance residual matrix. The root mean square value of the sum of the absolute values of all residuals in the covariance residual matrix and then taking the average is RMR; The comparative fit index CFI is the abbreviation of Confirmatory Fit Index. It is an indicator used to evaluate the goodness of fit of the model in Structural Equation Modeling (SEM). CFI is a comparative index used to compare the goodness of fit between the hypothesized model and the baseline model. Usually, it is hoped that the value of CFI is close to 1.0, the closer to 1.A value of 0 indicates a better fit between the model and the data; the calculation of CFI involves comparing the model fit. It uses the Comparative Fit Index (CFI) to compare the fit of the actual data and the baseline model. The CFI usually ranges from 0 to 1, and the closer it is to 1, the better the fit; the change range of the Normed Fit Index (NFI) is between 0 and 1, where 1 indicates a perfect fit. According to the regulations, an NFI less than 0.90 means the model needs to be reset, and the closer it is to 1, the better; the Non-Normed Fit Index (NNFI) is an important indicator for evaluating the model fit. The value of NNFI usually needs to be greater than 0.9 to be considered basically acceptable;.

[0083] Therefore, after calculating the chi-square to degrees of freedom, Goodness-of-Fit Index (GFI), Root Mean Square Error of Approximation (RMSEA), Root Mean Square Residual (RMR), Comparative Fit Index (CFI), Normed Fit Index (NFI), and Non-Normed Fit Index (NNFI) of the sensory processing model, it is required that the chi-square to degrees of freedom < 3, Goodness-of-Fit Index (GFI) > 0.9, Root Mean Square Error of Approximation (RMSEA) < 0.1, Root Mean Square Residual (RMR) < 0.05, Comparative Fit Index (CFI) > 0.9, Normed Fit Index (NFI) > 0.9, and Non-Normed Fit Index (NNFI) > 0.9. If any one of the indicators does not meet the requirements, the item with the least factor loading coefficient in the sensory processing model needs to be deleted until all the above-mentioned fit index criteria are simultaneously met. It is worth emphasizing that when deleting the items in the sensory processing model, the corresponding items in the scale factor model also need to be deleted synchronously to make the items included in the sensory processing model and the scale factor model consistent;

[0084] Next, continue to perform a credibility check on the scale factor model and the sensory processing model, that is, obtain the credibility of the respondent's answers to the scale questions (this data needs to be written by the target population to be measured when answering the theoretical evaluation scale questionnaire for each item's credibility). In this embodiment, the Cronbach's alpha coefficient is calculated based on the total score of all the remaining questions above. The Cronbach's alpha coefficient is a method for measuring the reliability of a scale or test and is the most commonly used reliability assessment tool in psychological or educational tests. The Cronbach's alpha coefficient is a set of commonly used methods for measuring the reliability of psychological or educational tests. It estimates the internal consistency of the test according to a certain formula and is used as an indicator of reliability. It measures the reliability of the "sum" of a set of synonymous or parallel tests. If the result of the Cronbach's alpha coefficient is greater than the standard value of 0.8, it indicates ideal reliability. If it is not greater than the standard value of 0.8, then the item with the smallest factor loading coefficient in the sensory processing model and the scale factor model needs to be further deleted until the result of the Cronbach's alpha coefficient is greater than the standard value of 0.8, as shown in the appendix Figure 6 shown;

[0085] Perform cross - correlation verification on the sensory processing model and the scale factor model, that is, obtain the total score of all items in any dimension of the sensory processing model, such as the tactile dimension. Then obtain the total score of all items in any dimension of the factor scale model, such as the total score of the sensation - seeking dimension. Calculate the correlation P - value of the total scores of these two dimensions. If the correlation P - value < 0.01, it means that these two dimensions are significantly correlated and meet the data criteria of this embodiment. If this standard is not met, it is necessary to synchronously delete the item with the smallest factor loading coefficient in the dimensions of the factor scale model and the sensory processing model that are cross - validated, so that the correlation P - value of any two cross - dimensions < 0.01. The cross - validation results are as shown in the appendix Figure 7 as shown;

[0086] The indicators of the scale factor model finally obtained in this embodiment are as shown in the appendix Figure 3 as shown, while the indicators obtained by the sensory processing model are as shown in the appendix Figure 5 as shown;

[0087] Convert the total score (raw score) of each finally selected question into a standard score, and form a comparable and usable standardized norm according to the normal distribution ranking of the standard scores;

[0088] Usage method for users: First, parents answer all the questions according to the item content selected above. Each question in the questionnaire is a multiple - choice question, and the score for each answer option is 1 - 5 points. Parents only need to select one option in the given options that can most accurately describe the behavioral characteristics of the child being tested to obtain the raw score of each item, and then obtain the total scores of each social behavior dimension and sensory processing dimension. Then, compare this score with the norm to generate a multi - dimensional factor evaluation result for the child's sensory processing development assessment.

[0089] Embodiment 2

[0090] Figure 8 is a system schematic diagram of a sensory processing assessment device for children aged 3 - 6 according to another exemplary embodiment. The device includes:

[0091] Initial scale generation module 1: used to generate an initial assessment scale according to the relevant items of the existing sensory processing theory, and screen the initial assessment scale according to the preset screening criteria to obtain a theoretical assessment scale;

[0092] Questionnaire score collection module 2: used to obtain the scores of each item in the theoretical assessment scale for the target test population and the credibility scores of each item;

[0093] KMO and Bartlett sphericity test module 3: used to add up the scores of the target test population for the same item to obtain the total score of each item in the theoretical evaluation scale, and respectively obtain the KMO value of the data quality test and the Bartlett sphericity test P value according to the total score of each item; at the same time, obtain the factor loading coefficients of each item and other items, and make the remaining items meet the KMO standard value and the Bartlett sphericity test standard value by deleting the item with a smaller factor loading coefficient;

[0094] Scale factor module 4: used to classify the items with factor loading coefficients greater than the preset factor loading threshold in the remaining items into the same group, and each group is a factor. Delete the factors that do not meet the preset conditions to obtain multiple target factors, and name each target factor according to the social behavior dimension to obtain a scale factor model composed of each factor;

[0095] Sensory processing model generation module 5: used to classify all the items included in the scale factor model into a preset multiple sensory processing dimensions to obtain a sensory processing model;

[0096] Fitting index screening module 6: used to judge the fitting index of the sensory processing model, and make multiple fitting indexes of the sensory processing model meet the preset index standard by deleting the item with the least factor loading coefficient, and synchronously delete the corresponding item in the scale factor model to make the items included in the scale factor model consistent with the sensory processing model;

[0097] Cronbach coefficient screening module 7: used to judge the Cronbach coefficient of the scale factor model and the sensory processing model according to the credibility scores of the target test population for each item, and make the sensory processing model and the scale factor model meet the preset coefficient standard value by deleting the item with the least factor loading coefficient;

[0098] Correlation coefficient screening module 8: used to perform cross-correlation verification on the scale factor model and the sensory processing model, and make the sensory processing model and the scale factor model meet the preset cross-correlation threshold by deleting the item with the least factor loading coefficient;

[0099] Standard norm conversion module 9: used to sort and convert the remaining items in the scale factor model or the sensory processing model into standard scores according to the scores of the target test population, and form a standardized norm according to the distribution of the standard scores;

[0100] Target test score module 10: It is used to obtain the original scores of each item in the scale factor model or the sensory processing model from the parents of children aged 3 - 6 to be tested, obtain the standard scores of each item according to the standardized norm, and obtain the social behavior dimension score and multiple sensory processing dimension scores based on the standard scores of each item.

[0101] Embodiment 3:

[0102] This embodiment provides a storage medium that stores a computer program. When the computer program is executed by a main controller, it implements each step in the above method;

[0103] It can be understood that the above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc.

[0104] It can be understood that the same or similar parts in the above embodiments can be referred to each other, and the content not detailed in some embodiments can be referred to the same or similar content in other embodiments.

[0105] It should be noted that in the description of the present invention, terms such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "a plurality of" refers to at least two.

[0106] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process. The scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a manner that is not shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by those skilled in the technical field to which the embodiments of the present invention belong.

[0107] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0108] Those of ordinary skill in the art can understand that all or part of the steps carried out in implementing the above-described embodiment methods can be completed by a program instructing relevant hardware. The program can be stored in a computer-readable storage medium, and when the program is executed, it includes one or a combination of the steps of the method embodiment.

[0109] In addition, in each of the embodiments of the present invention, the functional units can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0110] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, or the like.

[0111] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0112] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A sensory processing assessment method for children aged 3-6, characterized in that: The method comprises: generating an initial evaluation scale according to items related to existing sensory processing theories, and screening the initial evaluation scale according to preset screening criteria to obtain a theoretical evaluation scale; Obtaining the scores of the target test population for each item in the theoretical evaluation scale and the credibility score for each item; Add the scores of the target test population for the same item to obtain the total score of each item in the theoretical evaluation scale, and obtain the data quality inspection KMO value and Bartlett sphericity test P value according to the total score of each item; at the same time, obtain the factor loading coefficient of each item and other items, and delete the items with smaller factor loading coefficients so that the remaining items meet the KMO standard value and Bartlett sphericity test standard value at the same time; The items whose factor loading coefficients are greater than the preset factor loading threshold are classified into the same group through the maximum variance rotation method. Each group is a factor. The factors that do not meet the preset conditions are deleted to obtain multiple target factors. Each target factor is named according to the social behavior dimension to obtain the scale factor model composed of each factor. Classifying all items included in the scale factor model into a plurality of preset sensory processing dimensions to obtain a sensory processing model; The sensory processing model is judged for its fit index, and multiple fit indexes of the sensory processing model meet preset index standards by deleting the items with the least factor loading coefficients, and the corresponding items in the scale factor model are deleted simultaneously, so that the scale factor model is consistent with the items included in the sensory processing model; According to the credibility score of each item of the target test population, the Cronbach coefficient of the scale factor model and the sensory processing model is judged, and the items with the least factor loading coefficient are deleted to make the sensory processing model and the scale factor model meet the preset coefficient standard value; The scale factor model and the sensory processing model were cross-correlated verified, and the sensory processing model and the scale factor model were made to meet the preset cross-correlation threshold by deleting the items with the least factor loading coefficients. Each remaining item in the scale factor model or sensory processing model is sorted and converted into a standard score according to the scores of the target test population, and a standardized norm is formed according to the distribution of the standard scores; Obtain the original scores of each item in the scale factor model or sensory processing model from the parents of the children aged 3-6 years old to be tested, and obtain the standard score of each item according to the standardized norms, and obtain the social behavior dimension score and multiple sensory processing dimension scores according to the standard score of each item.

2. The method according to claim 1, characterized in that The initial evaluation scale is screened according to the preset screening criteria to obtain a theoretical evaluation scale including: Delete all items in the initial assessment scale that measure an age range that is not between 3 and 6 years old; Delete the relevant items containing the specified keywords in all the items of the initial assessment scale; the specified keywords are symptom keywords of the specified disease.

3. The method according to claim 2, characterized in that The target test population includes: The target test population includes N parents of children aged 3-6 in all target cities; The gender ratio and the urban and rural population ratio of the N parents meet the preset ratio standards; The 3-6-year-old children corresponding to the N parents are not children with designated diseases, and the 3-6-year-old children corresponding to the N parents are not attending special children's schools.

4. The method according to claim 3, characterized in that The step of deleting factors that do not meet the preset conditions to obtain multiple target factors includes: Get the number of items corresponding to each factor, and delete the items under the factors that are less than the preset number of items; Get the eigenvalue of each factor, and delete the items under the factors whose eigenvalues ​​do not meet the preset eigenvalues; Recalculate the factor loading coefficient of each item and other items for the remaining items after deleting the items, and classify the items whose factor loading coefficient is greater than the preset factor loading threshold into the same group through the maximum variance rotation method to obtain multiple new factors. The number of factors newly classified each time is less than the number of factors classified last time. Continue to judge whether the number of items and characteristic root values ​​corresponding to each factor meet the preset number of items and the preset characteristic root values. If not, continue to delete the items under the corresponding factor and repeatedly generate new factors until the number of items and characteristic root values ​​corresponding to each generated factor meet the preset number of items and the preset characteristic root values, and take the remaining factors as target factors.

5. The method according to claim 4, characterized in that The fitting index judgment of the sensory processing model is performed by deleting the items with the least factor loading coefficient so that multiple fitting indexes of the sensory processing model meet the preset index standards, including: Obtain multiple fit indicators of the sensory processing model, compare each fit indicator with the corresponding indicator standard, and if any fit indicator does not meet the preset indicator standard, delete an item with the smallest factor loading coefficient in the sensory processing model until the multiple fit indicators of the sensory processing model meet the preset indicator standard; The multiple fitting indices include chi-square ratio degrees of freedom, goodness of fit index GFI, root mean square of approximation error RMSEA, root mean square residual RMR, comparative fit index CFI, normed fit index NFI and non-normed fit index NNFI.

6. The method according to claim 5, characterized in that The Cronbach coefficient judgment of the scale factor model and the sensory processing model is performed according to the credibility score of each item of the target test population, and the sensory processing model and the scale factor model meet the preset coefficient standard value by deleting the items with the least factor loading coefficients. Obtain the total credibility score of the target test population for each item in the scale factor model or the sensory processing model, obtain the total credibility score of the target test population for all items based on the total credibility score of the target test population for each item, and obtain the Cronbach coefficient based on the total credibility score of the target test population for all items. If the Cronbach coefficient does not meet the preset coefficient standard value, then simultaneously delete the item with the smallest factor loading coefficient in the scale factor model and the sensory processing model, re-obtain the Cronbach coefficient, continue to judge whether the Cronbach coefficient meets the preset coefficient standard value, and repeat the item deletion process until the Cronbach coefficient meets the preset coefficient standard value.

7. The method according to claim 6, characterized in that The cross-correlation verification of the scale factor model and the sensory processing model is performed to make the sensory processing model and the scale factor model meet the preset cross-correlation threshold by deleting the items with the least factor loading coefficients, including: Obtain the correlation coefficient between any factor in the scale factor model and any dimension in the sensory processing model. If the correlation coefficient does not meet the preset correlation coefficient threshold, delete the item with the smallest factor loading coefficient in the scale factor model and the sensory processing model simultaneously, obtain the correlation coefficient again, continue to judge whether the correlation coefficient meets the preset correlation coefficient threshold, and repeat the item deletion process until the correlation coefficient meets the preset correlation coefficient threshold.

8. A sensory processing assessment device for children aged 3-6, characterized in that: The device comprises: An initial scale generation module: used to generate an initial evaluation scale based on items related to the existing sensory processing theory, and to screen the initial evaluation scale according to a preset screening standard to obtain a theoretical evaluation scale; Questionnaire score collection module: used to obtain the scores of the target test population for each item in the theoretical evaluation scale and the credibility score for each item; KMO and Bartlett sphericity test module: used to add the scores of the target test population for the same item, obtain the total score of each item in the theoretical evaluation scale, and obtain the data quality test KMO value and Bartlett sphericity test P value according to the total score of each item; at the same time, obtain the factor loading coefficient of each item and other items, and delete the items with smaller factor loading coefficients so that the remaining items meet the KMO standard value and Bartlett sphericity test standard value at the same time; Scale factor module: used to classify the remaining items whose factor loading coefficients are greater than the preset factor loading threshold into the same group through the maximum variance rotation method. Each group is a factor. Factors that do not meet the preset conditions are deleted to obtain multiple target factors. Each target factor is named according to the social behavior dimension to obtain the scale factor model composed of each factor. A sensory processing model generation module is used to classify all items included in the scale factor model into a plurality of preset sensory processing dimensions to obtain a sensory processing model; Fitting index screening module: used to judge the fitting index of the sensory processing model, by deleting the items with the least factor loading coefficient so that multiple fitting indexes of the sensory processing model meet the preset index standards, and simultaneously deleting the corresponding items in the scale factor model so that the scale factor model is consistent with the items included in the sensory processing model; Cronbach coefficient screening module: used to judge the Cronbach coefficient of the scale factor model and the sensory processing model according to the credibility score of each item of the target test population, and to make the sensory processing model and the scale factor model meet the preset coefficient standard value by deleting the items with the least factor loading coefficient; Correlation coefficient screening module: used to verify the cross-correlation of the scale factor model and the sensory processing model, and to make the sensory processing model and the scale factor model meet the preset cross-correlation threshold by deleting the items with the least factor loading coefficient; Standard norm conversion module: used to convert each remaining item in the scale factor model or sensory processing model into a standard score according to the normal distribution sorting of the target test population score, and form a standardized norm according to the distribution of the standard score; Target test scoring module: used to obtain the original scores of each item in the scale factor model or sensory processing model for parents of children aged 3-6 years old, and obtain the standard score of each item based on the standardized norms, and obtain the social behavior dimension score and multiple sensory processing dimension scores based on the standard score of each item.

9. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the main controller, each step of the sensory processing assessment method for children aged 3 to 6 years old as described in any one of claims 1 to 7 is implemented.

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