Family old-age care ability assessment method, device, equipment, medium and program

Abstract

The invention relates to the technical field of endowment service evaluation, in particular to a family endowment ability evaluation method, device, equipment, medium and program, and the method comprises the steps: inputting life condition data, nursing support data and emotion support data into a trained target language model, calling a pre-established service evaluation system by the target language model, mapping the life condition data, the care support data and the emotion support data to pre-established evaluation indexes in a family old-age care ability evaluation system, and calculating a quantitative score of the corresponding evaluation index according to mapping data corresponding to each evaluation index; and identifying a weight corresponding to each evaluation index, and generating a family old-age care ability evaluation result according to the quantitative score of each evaluation index and the corresponding weight. Therefore, the problems of low data processing efficiency, one-sided evaluation dimension, insufficient evaluation result precision, large error and the like caused by a manual evaluation mode in related technologies are solved.

Description

Technical Field

[0001] This application relates to the field of elderly care service assessment technology, and in particular to a method, device, equipment, medium and procedure for assessing family-based elderly care capabilities. Background Technology

[0002] With the accelerating aging of the population, the number and proportion of elderly people are growing rapidly, and their multi-level needs for health maintenance, livelihood security, and social participation are becoming increasingly prominent. Traditional elderly care models are no longer sufficient to meet the multi-level and multi-dimensional needs of the elderly. Instead, a diversified care support system is needed, centered on the family, supported by the community, and supplemented by social institutions and social forces. In this system, the family is not only the primary space for the elderly's daily life but also the first line of defense for health care. The strength of family-based elderly care capabilities directly affects the elderly's physical and mental health and life satisfaction, and also determines whether community-based age-friendly services can play their due role.

[0003] The strength of family-based elder care capabilities directly impacts the physical and mental health of the elderly, their life satisfaction, and their effective utilization of community and public service resources. A scientific and systematic assessment of family-based elder care capabilities not only helps identify high-risk elderly households and accurately match them with age-appropriate services, but also provides empirical evidence for the optimal allocation of public elder care resources and the formulation of national elder care policies.

[0004] In related technologies, the assessment methods for family-based elder care capabilities mainly rely on manual methods such as questionnaires, interviews, and expert scoring. However, manual processing requires a lot of time and human resources, cannot achieve rapid calculation and real-time feedback, and has a long assessment cycle and delayed updates. Manual statistics and experience judgment are also easily affected by cognitive biases, resulting in high assessment errors and making it difficult to accurately reflect the true level of service. Summary of the Invention

[0005] This application provides a method, device, equipment, medium, and program for assessing family-based elderly care capabilities, in order to solve the problems of low data processing efficiency, one-sided evaluation dimensions, insufficient accuracy of evaluation results, and large errors caused by manual evaluation methods in related technologies.

[0006] The first aspect of this application provides a method for assessing family-based elderly care capacity, comprising the following steps: acquiring living condition data, care support data, and emotional support data from family-based elderly care capacity data; inputting the living condition data, care support data, and emotional support data into a trained target language model, wherein the target language model invokes a pre-established service evaluation system to map the living condition data, care support data, and emotional support data to pre-established evaluation indicators under the family-based elderly care capacity evaluation system, and calculating the quantitative score of the corresponding evaluation indicator based on the mapping data corresponding to each evaluation indicator; identifying the weight corresponding to each evaluation indicator, and generating a family-based elderly care capacity assessment result based on the quantitative score of each evaluation indicator and its corresponding weight.

[0007] Optionally, the processing method of the target language model includes: extracting at least one feature vector from the living conditions data, the care support data, and the emotional support data; mapping the living conditions data, the care support data, and the emotional support data to pre-established assessment indicators under the family-based elderly care capacity evaluation system based on the semantic similarity between the feature vectors and the assessment indicators; and determining the quantitative score corresponding to each assessment indicator based on the mapping data corresponding to each assessment indicator and the reference data of each assessment indicator.

[0008] Optionally, determining the quantitative score corresponding to each evaluation indicator based on the mapping data corresponding to each evaluation indicator and the reference data of each evaluation indicator includes: determining the level of the evaluation indicator corresponding to the mapping data of each evaluation indicator based on the mapping data of each evaluation indicator and the reference data of each evaluation indicator; and determining the quantitative score corresponding to each evaluation indicator based on the level of the evaluation indicator corresponding to the mapping data of each evaluation indicator.

[0009] Optionally, determining the quantitative score corresponding to each evaluation indicator based on the level of the evaluation indicator corresponding to the mapping data corresponding to each evaluation indicator includes: obtaining a mapping relationship table, wherein the mapping relationship table is a mapping relationship between the level of the evaluation indicator corresponding to the mapping data corresponding to each evaluation indicator and the quantitative score corresponding to each evaluation indicator; querying the mapping relationship table using the level of the evaluation indicator as an index to determine the quantitative score corresponding to each evaluation indicator.

[0010] Optionally, the family-based elderly care capacity evaluation system includes: a first criterion layer to a third criterion layer, each criterion layer comprising multiple service indicators. The first criterion layer is the living conditions capacity layer, the second criterion layer is the care support capacity layer, and the third criterion layer is the emotional support capacity layer. The first criterion layer includes a first assessment indicator, a second assessment indicator, and a third assessment indicator. The first assessment indicator is the average monetary assets per elderly person, the second assessment indicator is the proportion of medical expenses to household consumption expenditure, and the third assessment indicator is the housing level. The second criterion layer includes a fourth assessment indicator and a fifth assessment indicator. The fourth assessment indicator is the average daily care time, and the fifth assessment indicator is care skills. The third criterion layer includes a sixth assessment indicator, a seventh assessment indicator, and an eighth assessment indicator. The sixth assessment indicator is communication and interaction, the seventh assessment indicator is decision-making participation, and the eighth assessment indicator is the conflict occurrence rate.

[0011] Optionally, identifying the weight corresponding to each evaluation indicator includes: obtaining any two evaluation indicators from each criterion layer of the family elder care capacity evaluation system and inputting them into an expert model, with the expert model outputting the corresponding importance score; determining the consistency verification result of the importance score; if the consistency verification result is less than or equal to a preset threshold, calculating the weight corresponding to each evaluation indicator based on the importance score; if the consistency verification result is greater than the preset threshold, iteratively evaluating the importance score of each evaluation indicator using the expert model until the consistency verification result is less than or equal to the preset threshold, and then calculating the weight corresponding to each evaluation indicator based on the current importance score.

[0012] A second aspect of this application provides a family-based elderly care capacity assessment device, comprising: an acquisition module for acquiring living condition data, care support data, and emotional support data from family-based elderly care capacity data; a processing module for inputting the living condition data, care support data, and emotional support data into a trained target language model, wherein the target language model invokes a pre-established service evaluation system to map the living condition data, care support data, and emotional support data to pre-established evaluation indicators under the family-based elderly care capacity evaluation system, and calculates a quantitative score for each evaluation indicator based on the mapping data corresponding to each evaluation indicator; and an identification module for identifying the weight corresponding to each evaluation indicator and generating a family-based elderly care capacity assessment result based on the quantitative score of each evaluation indicator and its corresponding weight.

[0013] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the family-based elderly care capacity assessment method as described in the above embodiments.

[0014] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to perform the family-based elderly care capacity assessment method as described in the above embodiments.

[0015] A fifth aspect of this application provides a computer program product, including a computer program or instructions, which, when executed, implement the family-based elderly care capacity assessment method as described in the above embodiments.

[0016] Therefore, this application has at least the following beneficial effects: This application embodiment can acquire data on the living conditions, care support, and emotional support of elderly people's families, and input these data into a trained target language model. Based on a pre-constructed family elder care capacity evaluation system, the model automatically maps the raw data to corresponding evaluation indicators and calculates the quantitative scores of each indicator according to standardized scoring rules. Combined with the indicator weights determined by the analytic hierarchy process and the Delphi method, a scientific, objective, and comparable comprehensive evaluation result of family elder care capacity is finally generated. This realizes the automation, quantification, and intelligence of family elder care capacity evaluation, significantly improving the evaluation efficiency and accuracy, and providing reliable technical support for precise matching of community elder care services, optimization of public resource allocation, and smart elder care decision support.

[0017] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0018] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a family-based elderly care capacity assessment method provided according to an embodiment of this application; Figure 2 This is a block diagram of a family-based elderly care capacity assessment device provided according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0019] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0020] In recent years, research on the evaluation of family-based elder care capacity has yielded some results both domestically and internationally. However, current research on this evaluation often focuses on a single aspect, such as material input (e.g., economics) or caregiving ability. There is currently no systematic evaluation system for family-based elder care capacity, either domestically or internationally. Existing research findings provide a foundation for developing such an evaluation system.

[0021] The economic support capacity of elderly families is the foundation and guarantee for ensuring the health and high quality of life of the elderly. Per capita monetary assets are an important indicator of a family's financial situation and a basic basis for determining whether a family can apply for the national minimum living allowance. Therefore, the per capita monetary assets of the elderly (including savings, cash, investments, stocks, funds, and assets readily convertible into cash, such as those provided by children) are an important indicator of their economic support capacity for old-age care. Research has found that when the proportion of elderly people's medical expenses to household consumption expenditure (A1) reaches more than 40%, it constitutes "catastrophic medical expenditure." This threshold often means that the elderly will face severe limitations in meeting their basic living needs. High proportions of medical expenditure not only weaken the elderly's ability to spend on nutrition, housing, and social participation but also directly reduce their life satisfaction and happiness. More importantly, high proportions of medical expenditure often squeeze the elderly's investment space in home renovation and access to age-friendly community services, making it difficult for them to obtain multi-dimensional support in a timely manner, thus affecting health management and functional maintenance. Furthermore, the elderly's living conditions are a fundamental indicator of their economic capacity. Therefore, per capita monetary assets, the proportion of medical expenditure, and living conditions are three important basic indicators for measuring the economic support capacity of elderly families for old-age care.

[0022] Family caregiving capacity is a core factor influencing the health and quality of life of disabled older adults. Care not only concerns basic living support but also directly determines the effectiveness of chronic disease management, functional maintenance, and psychological well-being. Existing research indicates that the average daily home care time for disabled older adults in Canada is approximately 2.07 hours, while in the United States it is 20 hours per week, or about 2.9 hours per day. These figures provide a general "minimum reference standard" for family caregiving of disabled older adults globally, namely 2–3 hours per day. Insufficient home care time or inadequate caregiver skills can lead to a decline in care quality, exacerbate health risks for older adults, and potentially increase unnecessary use of medical resources. Therefore, family caregiving capacity (caregiving time and caregiving skills) is another important dimension for measuring the family's ability to provide elder care.

[0023] Emotional support plays a central role in the physical and mental health of older adults, especially those experiencing loneliness, functional decline, or mental health issues. Emotional support significantly mitigates the negative health effects of these conditions. Emotional support capabilities are primarily manifested in three aspects: communication and interaction skills, decision-making participation, and conflict resolution. Multiple psychological and gerontological studies have shown that older adults can effectively slow cognitive decline and depression through communication and interaction, especially face-to-face communication. Loneliness is closely associated with depression, cognitive decline, and deteriorating physical health in older adults. Communication and interaction, particularly face-to-face or video communication among family members, provides nonverbal cues (such as facial expressions, touch, and body language), significantly enhancing the quality of emotional connection and thus promoting mental health. Furthermore, decision-making participation is closely related to the autonomy and dignity of older adults. Autonomy is considered a core value in healthcare for older adults, emphasizing the individual's right and ability to choose in the decision-making process. The shared decision-making model proposed by Charles et al. indicates that high participation in treatment not only enhances patients' sense of control over their treatment but also improves their adherence to treatment and satisfaction, ultimately leading to better treatment outcomes and quality of life. Furthermore, the incidence of family conflict is a significant negative factor affecting emotional support within families for the elderly, profoundly impacting their mental health and quality of life. Studies show that prolonged periods of cold war have a significant impact on the mental health of the elderly, potentially leading to problems such as loneliness, depression, and cognitive decline. Frequent conflicts erode emotional support, disrupt family harmony, inhibit participation in decision-making, and hinder communication and negotiation. Therefore, the elderly's capacity to provide emotional support within their families is a high-level dimension in measuring their ability to provide care for the elderly within their families.

[0024] In summary, the three dimensions of family economic support capacity, care support capacity, and emotional support capacity all have a significant impact on the health and quality of life of the elderly. However, currently, there is no systematic evaluation index system for the family-based elderly care capacity that includes these three dimensions, both domestically and internationally. This is detrimental to the scientific allocation of public elderly care service resources.

[0025] The following description, with reference to the accompanying drawings, describes the family-based elderly care capacity assessment method, apparatus, equipment, medium, and procedure according to embodiments of this application.

[0026] Specifically, Figure 1 This is a flowchart illustrating a family-based elderly care capacity assessment method provided in an embodiment of this application.

[0027] like Figure 1 As shown, this method for assessing family-based elder care capacity includes the following steps: In step S101, the data on living conditions, care support, and emotional support from the family elder care capacity data are obtained.

[0028] It is understood that the embodiments of this application can obtain data on living conditions, care support, and emotional support in the family elderly care capacity data. The data acquisition method covers key dimensions such as economic foundation (e.g., per capita monetary assets, housing conditions), care supply (e.g., average daily care time, care skill level), and emotional interaction (e.g., communication frequency, decision-making participation, family conflicts), ensuring the comprehensiveness and authenticity of the assessment input, laying a high-quality data foundation for the subsequent accurate quantification of family elderly care capacity, and effectively overcoming the problems of fragmented information, strong subjectivity, and incomplete coverage in traditional assessments.

[0029] In step S102, the living conditions data, care support data, and emotional support data are input into the trained target language model. The target language model calls the pre-established service evaluation system to map the living conditions data, care support data, and emotional support data to the pre-established evaluation indicators under the family elderly care capacity evaluation system, and calculates the quantitative score of the corresponding evaluation indicator based on the mapping data corresponding to each evaluation indicator.

[0030] The family-based elderly care capacity evaluation system includes three criterion layers: the first criterion layer, the second criterion layer, and the third criterion layer. Each criterion layer contains multiple service indicators. The first criterion layer is the living conditions capacity layer, the second criterion layer is the care support capacity layer, and the third criterion layer is the emotional support capacity layer. The first criterion layer includes the first assessment indicator, the second assessment indicator, and the third assessment indicator. The first assessment indicator is the average monetary assets per elderly person, the second assessment indicator is the proportion of medical expenses to household consumption expenditure, and the third assessment indicator is the housing level. The second criterion layer includes the fourth assessment indicator and the fifth assessment indicator. The fourth assessment indicator is the average daily care time, and the fifth assessment indicator is care skills. The third criterion layer includes the sixth assessment indicator, the seventh assessment indicator, and the eighth assessment indicator. The sixth assessment indicator is communication and interaction, the seventh assessment indicator is decision-making participation, and the eighth assessment indicator is the conflict occurrence rate.

[0031] It is understood that the embodiments of this application can input the collected living conditions data, care support data, and emotional support data into the trained target language model. The model can then automatically call the pre-built family elder care capacity evaluation system to achieve intelligent mapping of raw, multi-source, heterogeneous data to standardized evaluation indicators. Based on preset scoring rules, the quantitative scores of each indicator are accurately calculated, which significantly improves the automation level and consistency of data analysis and indicator transformation, avoids subjective bias in human interpretation, and ensures that the evaluation results are objective, comparable, and reproducible. This provides key technical support for the efficient and accurate quantification of family elder care capacity.

[0032] Specifically, the family-based elderly care capacity assessment index system includes three criterion layers: economic support capacity, care support capacity, and emotional support capacity. Each criterion layer contains specific assessment indicators for family-based elderly care capacity. Among them, the economic support capacity criterion layer includes three assessment indicators: per capita monetary assets of the elderly (A1), the proportion of medical expenditure to household consumption expenditure (A2), and housing level (A3); the care support capacity criterion layer includes two assessment indicators: average daily care time (B1) and care skills (B2); and the emotional support capacity criterion layer includes three indicators: communication and interaction (C1), decision-making participation (C2), and conflict occurrence rate (C3).

[0033] In this embodiment of the application, the processing method of the target language model includes: extracting at least one feature vector from living condition data, care support data, and emotional support data; mapping the living condition data, care support data, and emotional support data to pre-established assessment indicators under the family elderly care capacity evaluation system based on the semantic similarity between the feature vectors and the assessment indicators; and determining the quantitative score corresponding to each assessment indicator based on the mapping data corresponding to each assessment indicator and the reference data of each assessment indicator.

[0034] It is understood that the embodiments of this application can perform deep semantic understanding of living condition data, care support data, and emotional support data through a target language model, extract their corresponding feature vectors, and achieve intelligent and accurate mapping from data to indicators based on the semantic similarity between the feature vectors and the various assessment indicators in the family elderly care capacity evaluation system. Then, by combining the mapping data corresponding to each assessment indicator with its preset reference threshold or benchmark data, a standardized quantitative score is automatically calculated, which effectively solves the problem that unstructured or semi-structured elderly care data is difficult to directly match with evaluation indicators, improves the accuracy of indicator mapping and the objectivity of scoring, and realizes end-to-end intelligent transformation from raw family data to quantifiable elderly care capacity assessment results.

[0035] For example, suppose we have the following descriptive text (i.e., raw data) about Mr. Li's family: "Mr. Li lives with his son. His monthly disposable income is about 4,000 yuan, while the local average is 3,000 yuan. Monthly medical expenses account for 20% of the family's total consumption. His son spends about 3 hours a day assisting him with his daily life and medication. He has mastered skills such as giving medicine and taking blood pressure, but he does not know how to handle emergencies. The family can have video calls with him several times a week and discuss important matters together, but there have been two disputes this month due to the way he is cared for." Step 1: Extract feature vectors The model first understands the semantics of the text and extracts key information, transforming it into structured feature vectors. Through mechanisms such as self-attention, the model identifies key entities, values, and behaviors in the text, generating one or more high-dimensional, digitized feature vectors that encapsulate the semantic information of the text. Key features extracted might include: per capita monetary wealth ratio: 1.33, medical expenditure ratio: 0.20, average daily care duration: 3 hours, mastery of basic skills: True, mastery of medical skills: True, mastery of emergency skills: False, video communication frequency: medium, decision-making mode: collaborative decision-making, number of verbal disputes: 2.

[0036] Step 2: Semantic mapping to evaluation metrics The model performs semantic similarity matching between the aforementioned feature vectors and preset evaluation metrics, thereby merging the messy features into a standard metric system. Specifically, the model has an internal knowledge base that maps metrics to features, and it calculates which metric description is most relevant to each feature, thus classifying the original data and mapping it to the corresponding evaluation metrics.

[0037] Specifically: the characteristic data mapped by the per capita monetary wealth (A1) indicator is: the ratio of the per capita monetary wealth of the elderly to the local average level is 1.33; the characteristic data mapped by the medical expenditure ratio (A2) indicator is: the proportion of medical expenditure of the elderly to the total household consumption expenditure is 0.20 (i.e. 20%).

[0038] The characteristic data mapped by the daily average care duration (B1) indicator is: the average daily care duration provided by family members to the elderly is 3 hours. The characteristic data mapped by the care skills (B2) indicator is a structured set of skills, specifically: basic skills (mastered), medical skills (mastered), emergency skills (not mastered), and cognitive care skills (not mastered).

[0039] The characteristic data mapped by the Communication and Interaction (C1) indicator is that the frequency of video communication between family members and the elderly is rated at a "moderate" level. The characteristic data mapped by the Decision-Making Participation (C2) indicator is that the elderly's decision-making pattern in major family matters is identified as "joint decision-making." The characteristic data mapped by the Conflict Incidence (C3) indicator is that there were 2 instances of verbal disputes within the family this month.

[0040] Step 3: Determine the quantitative score The model calculates the final quantitative score based on the specific data mapped to each indicator, referring to the preset scoring criteria and mapping relationship table.

[0041] In this embodiment of the application, the quantitative score corresponding to each evaluation indicator is determined based on the mapping data and reference data of each evaluation indicator, including: determining the level of the evaluation indicator corresponding to the mapping data of each evaluation indicator based on the mapping data and reference data of each evaluation indicator; and determining the quantitative score corresponding to each evaluation indicator based on the level of the evaluation indicator corresponding to the mapping data of each evaluation indicator.

[0042] It is understood that the embodiments of this application can automatically determine the assessment level of each assessment indicator by comparing the mapping data corresponding to each assessment indicator with its preset reference data, and determine the final score according to the mapping rules between the level and the quantitative score. The graded scoring mechanism integrates multi-dimensional empirical evidence to ensure that the scoring standards are scientific, have clear boundaries, and are highly interpretable. At the same time, it realizes the standardization and automation of the assessment process, effectively avoids the subjectivity and inconsistency of human judgment, and significantly improves the objectivity, comparability and policy applicability of the family elderly care capacity assessment results.

[0043] In this embodiment of the application, the quantitative score corresponding to each evaluation indicator is determined based on the level of the evaluation indicator corresponding to the mapping data corresponding to each evaluation indicator, including: obtaining a mapping relationship table, wherein the mapping relationship table is the mapping relationship between the level of the evaluation indicator corresponding to the mapping data corresponding to each evaluation indicator and the quantitative score corresponding to each evaluation indicator; using the level of the evaluation indicator as an index, querying the mapping relationship table to determine the quantitative score corresponding to each evaluation indicator.

[0044] It is understood that the embodiments of this application can establish a clear and structured correspondence between the level of each assessment indicator and its corresponding standardized quantitative score by pre-constructing a mapping relationship table. During the assessment process, the determined indicator level is used as an index to directly query the mapping relationship table and quickly determine the quantitative score of each assessment indicator. This realizes the explicitness of the scoring rules, the automation of the process, and the consistency of the results, effectively avoiding subjective judgment bias and significantly improving the efficiency, accuracy, and reproducibility of family elderly care capacity assessment. It also provides reliable technical support for large-scale, intelligent elderly care assessment applications.

[0045] Specifically, different mapping relationships are applied to different evaluation indicators. (1) Construction of evaluation criteria for per capita monetary assets (A1) of the elderly The evaluation standard for the average monetary assets of the elderly (A1) is formulated with reference to the average monetary assets of local residents. The full score is 4 points. When the ratio of the average monetary assets of the elderly to the average monetary assets of local residents (a) is less than 0.4, it is set to 0 points; 0.4 ≤ a < 0.8, it is set to 1 point; 0.8 ≤ a < 1.2, it is set to 2 points; 1.2 ≤ a < 1.6, it is set to 3 points; and a ≥ 2, it is set to 4 points.

[0046] (2) Construction of evaluation criteria for the proportion of elderly people’s medical expenses in household consumption expenditure (A2) Following a triple evidence chain of "international threshold—Chinese average—risk gradient," an evaluation standard for the proportion of elderly people's medical expenditure in household consumption expenditure was established. The maximum score is 4 points. The international disaster medical expenditure threshold (A2=40%) has been widely used in health financing studies in more than 100 countries and has universal comparability. Therefore, an A2 of 0.35-0.45 is set as 1 point (high-risk warning), and A2 ≥ 0.45 is set as 0 points (crossing the red line). Referring to the national average (A2=22%), A2=0.22 is taken as the median of the "reasonable range," so an A2 of 0.15-0.25 is used as the core reference band of 3 points. According to the risk gradient, when A2 is between 0 and 0.15, it is below the average and far from the A2 threshold, indicating very little economic pressure, and a maximum score of 4 points is obtained. When A2 is between 0.25 and 0.35, the score drops to 2 points.

[0047] (3) Evaluation criteria for the living conditions of the elderly (A3) A quantitative evaluation standard for living conditions was established using the ratio (γ) of the per capita living space of the elderly to the per capita living space of local residents as the core indicator, with a maximum score of 4 points. The specific scoring rules are as follows: γ < 0.4, 0 points; 0.4 ≤ γ < 0.8, 1 point; 0.8 ≤ γ < 1.2, 2 points; 1.2 ≤ γ < 1.6, 3 points; γ ≥ 2, 4 points.

[0048] (4) Construction of evaluation criteria for average daily care duration (B1) The reference standard for daily care duration B1 is as follows: For disabled elderly people, B1 < 2 hours means insufficient care, 2-4 hours is basic guarantee, 4-8 hours is a reasonable and ideal range, and > 8 hours indicates potential risks to caregivers. Therefore, referring to international data and policy standards, and taking into account both minimum care needs and sustainability risks, the daily care duration is set at five levels, with a maximum score of 4 points: < 0.5 hours (0 points), 0.5-2 hours (1 point), 2-4 hours (2 points), 4-8 hours (3 points), and > 8 hours (4 points).

[0049] (5) Construction of evaluation criteria for care skills (B2) Caregiver skills assessment should cover four dimensions: basic skills, medical skills, emergency skills, and cognitive care skills, to ensure that older adults receive comprehensive support in terms of daily life, health, and mental well-being. These four skill dimensions complement and reinforce each other, collectively forming a comprehensive competency framework for caregivers in elderly care, with a maximum score of 4 points.

[0050] In terms of weighting, this system is calibrated based on the "event frequency - risk consequences" characteristics of skill application. Basic skills and medical skills belong to the "high frequency - high consequences" category; errors in these areas will directly impact the daily lives of the elderly and the safety management of chronic diseases. Therefore, each of these two skills is assigned 1.4 points, totaling 2.8 points, to match high-risk scenarios with high weighting. In contrast, emergency skills and cognitive care skills, while equally important, belong to the "low frequency - high impact" category. Assigning too high a value to these skills could create an excessively high evaluation threshold, weakening their feasibility in home, community, and institutional care scenarios. Therefore, this system assigns 0.6 points to each of these skills, totaling 1.2 points, to balance quality control and practical accessibility.

[0051] (6) Construction of evaluation criteria for communication and interaction (C1) The assessment of communication and interaction is scored out of 4 points, evaluated monthly. The weighting of the scoring criteria follows the principle of "interaction intensity." Face-to-face or video communication provides nonverbal cues such as facial expressions, body language, and touch, significantly reducing the risk of loneliness, depression, and cognitive decline; therefore, each interaction is assigned 0.5 points to reflect its "high intimacy—high benefit" characteristic. Telephone communication lacks nonverbal information but can still significantly alleviate loneliness and depression; therefore, each interaction is assigned 0.3 points, with a maximum total score of 3 points, reflecting its "second-best but effective" supplementary role.

[0052] (7) Construction of evaluation criteria for decision-making participation (C2) According to the "Shared Decision Continuum" model, "autonomous decision-making" is placed at the highest position and given a full score of 4 points; "joint decision-making" is given 3 points; "informed consent" is given 2 points; "passive awareness" is given 1 point; and "complete absence" is placed at the lowest position and given 0 points.

[0053] (8) Construction of evaluation criteria for conflict occurrence rate (C3) The assessment combines family conflict with elderly care scenarios, refining the scoring criteria for conflict types and intensity. The assessment of conflict incidence rate uses the absence of conflict as the maximum score (4 points), with points deducted for each conflict that occurs each month, and a minimum score of 0 points.

[0054] The specific deduction mechanism is as follows: Verbal disputes (deduction standard: -0.5 points / time / month) are considered mild conflicts, with relatively low destructiveness; 0.5 points are deducted for each verbal dispute per month. Behavioral confrontations (deduction standard: -1 point / time / month) are moderate conflicts, mainly referring to pushing, shoving, and confrontational behavior, which may pose a direct threat to the safety of the elderly and family relationships. Prolonged cold wars (deduction standard: -2 points / time / month) are high-intensity or long-term conflicts, manifested as prolonged emotional isolation and indifference.

[0055] This evaluation system, through its detailed division of various dimensions and scoring rules, achieves a comprehensive and objective assessment of family-based elder care capabilities. This not only improves the accuracy and operability of the assessment results but also provides strong support for precise elder care services.

[0056] For example, this evaluation system covers three target layers: economic support capacity, care support capacity, and emotional support capacity. Each target layer has specific evaluation indicators and detailed scoring criteria. The quantitative score for each evaluation indicator needs to be obtained by consulting the mapping table, as shown in Table 1 below.

[0057] Table 1

[0058] For example, Mr. Li: 75 years old, mildly disabled, lives with his son and daughter-in-law. Caregivers: son (primarily responsible) and daughter-in-law (assisting).

[0059] (1) Assessment of economic support capacity A1: Average Monetary Assets of the Elderly Data: The funds provided to Mr. Li and his family are 4,000 yuan per person per month. The average monthly monetary assets of local residents are 3,000 yuan. Calculation: Ratio a = 4000 / 3000 ≈ 1.33. Table score: 1.2 ≤ a (1.33) < 1.6, according to Table 1, 3 points are awarded.

[0060] A2: The proportion of medical expenses in household consumption expenditure Data: Mr. Li's monthly medical expenses are approximately 1200 yuan, and the family's total consumption expenditure is 6000 yuan. Calculation: A2 = 1200 / 6000 = 0.20 (i.e., 20%); Table score: 0.15 ≤ A2 (0.20) < 0.25, according to Table 1, 3 points are awarded.

[0061] A3: Living conditions Data: The average living space per capita in Mr. Li's family is 35 square meters. The average living space per capita in the local area is 30 square meters. Calculation: Ratio γ = 35 / 30 ≈ 1.17; Table score: 0.8 ≤ γ (1.17) < 1.2, according to Table 1, 2 points are awarded.

[0062] Subtotal of economic support capacity: A1(3) + A2(3) + A3(2) = 8 points (the full score for this criterion is 12 points).

[0063] (2) Assessment of care support capacity B1: Average daily care duration Data: The son provides care for Mr. Li for an average of about 3 hours a day (such as feeding medicine, bathing, and companionship). According to the table, 2h≤B1(3h)<4h, and according to Table 1, he gets 2 points.

[0064] B2: Caregiving Skills Data: The assessment showed that the son had mastered basic skills (such as bathing assistance) and medical skills (such as measuring blood pressure), but lacked emergency skills (such as CPR) and cognitive care skills (such as cognitive training). Scoring based on Table 1: Basic skills (1.4 points) + Medical skills (1.4 points) + Emergency skills (0 points) + Cognitive care skills (0 points) = 2.8 points.

[0065] Subtotal of care support capacity: B1(2) + B2(2.8) = 4.8 points (the maximum score for this criterion is 8 points) (3) C1: Communication and interaction Data: This month, the son had 12 face-to-face interactions and 15 phone conversations with Mr. Li. Mr. Li lives alone (this is to demonstrate individual difference coefficients, which is inconsistent with the original background), so a coefficient of ×1.2 is applied.

[0066] Calculation: Face-to-face score: 12 times × 0.5 points / time = 6 points; Telephone score: 15 times × 0.3 points / time = 4.5 points > 3 points, so take 3 points. Raw total score = 6 + 3 = 9 points > 4 points (full score), so take 4 points. Applying the coefficient of individual difference: Mr. Li lives alone, total score 4 points × 1.2 = 4.8 points (but the final score cannot exceed the full score of 4 points), so the final score is 4 points.

[0067] C2: Decision-making participation Data: Regarding Mr. Li's medical plan, his son will inform him in detail and discuss and decide together. Table Score: This falls under "joint decision-making," and according to Table 1, it scores 3 points.

[0068] C3: Conflict Occurrence Rate Data: This month, due to caregiving issues, the son and Mr. Li had two verbal arguments and one period of cold war lasting approximately two days. Calculation: Base score: 4 points; Deductions: 2 verbal arguments × (-0.5 points / argument) = -1 point; 1 period of cold war × (-2 points / argument) = -2 points. Final score: 4 - 1 - 2 = 1 point.

[0069] Subtotal of emotional support ability: C1(4) + C2(3) + C3(1) = 8 points (the full score for this criterion is 12 points).

[0070] Therefore, the final total score for family-based elder care capability (unweighted) is: 8 + 4.8 + 8 = 20.8 points (out of a total of 32 points). This assessment clearly shows that Mr. Li's family performs relatively well in terms of economic support and emotional communication (excluding conflicts); however, there are safety risks associated with emergency response and cognitive care skills. Recommendations include: providing professional skills training for caregivers; and for mildly disabled elderly individuals, appropriately increasing or optimizing the allocation of care time.

[0071] In step S103, the weight corresponding to each assessment indicator is identified, and the family elderly care capacity assessment result is generated based on the quantitative score of each assessment indicator and its corresponding weight.

[0072] It is understood that the embodiments of this application can identify the predetermined scientific weights of each assessment indicator in the family-based elderly care capacity evaluation system, and weight and integrate the quantitative scores of each indicator with their corresponding weights to generate a comprehensive and comparable family-based elderly care capacity assessment result. This fully reflects the relative importance of different dimensions and specific indicators in the overall elderly care capacity, making the assessment result not only reflect objective data, but also fit the actual elderly care needs and policy orientation, significantly improving the scientific nature, accuracy and decision support value of the assessment, and providing a solid basis for the matching of personalized elderly care services and the optimal allocation of public resources.

[0073] In this embodiment, identifying the weight corresponding to each evaluation indicator includes: obtaining any two evaluation indicators from each criterion layer of the family elder care capacity evaluation system and inputting them into an expert model; the expert model outputs the corresponding importance score; determining the consistency verification result of the importance score; if the consistency verification result is less than or equal to a preset threshold, then calculating the weight corresponding to each evaluation indicator based on the importance score; if the consistency verification result is greater than the preset threshold, then using the expert model to iteratively evaluate the importance score of each evaluation indicator until the consistency verification result is less than or equal to the preset threshold, then calculating the weight corresponding to each evaluation indicator based on the current importance score.

[0074] The preset threshold can be set according to actual needs without specific limitations.

[0075] It is understood that the embodiments of this application can introduce an expert model to compare any two evaluation indicators in each criterion layer of the family elder care capacity evaluation system, obtain their relative importance scores, and judge the rationality of the scoring logic based on a consistency verification mechanism: if the consistency verification result does not exceed the preset threshold, the weight of each indicator is directly calculated accordingly; if the verification fails, the expert model is driven to iteratively optimize the score until the consistency requirement is met before the final weight is determined. By integrating the expert consensus of the Delphi method and the mathematical rigor of the analytic hierarchy process, the scientific nature, logical consistency and domain applicability of the weight allocation are effectively guaranteed, the arbitrariness of subjective weighting is avoided, and the credibility and policy guidance value of the family elder care capacity assessment results are significantly improved.

[0076] Specifically, when determining the weights of each indicator in the family-based elder care capacity evaluation index system, a hybrid weighting method combining the Analytic Hierarchy Process (AHP) and the Delphi method is adopted to balance expert experience with mathematical rigor, ensuring the scientific validity and consistency of the weighting results. The specific process is as follows: (1) First, the Delphi method was used to collect expert opinions. Experts were selected from relevant fields such as service design and elderly care to form an evaluation expert group. The experts' views on the importance of different indicators in the family elderly care capacity assessment indicator system were investigated, and the importance of each indicator (pairwise comparison) was established and scored (using the 1-9 scale to represent the relative importance between indicators).

[0077] (2) Construct the Analytic Hierarchy Process (AHP) model. Determine the hierarchical structure design, including the target layer: determine the final goal (level of family elder care ability); the criteria layer: define the main criteria affecting the evaluation of family elder care ability (economic support ability, care support ability, emotional support ability); and the indicator layer: each criterion can be further subdivided into specific indicators (a total of 8 specific indicators). (3) Use the AHP software to calculate the weight of each indicator. Input the constructed hierarchical structure into the AHP software, including the target layer, criteria layer, and indicator layer, to form a complete evaluation indicator system. Input the importance evaluation scores of the expert group for each indicator (pairwise comparison) into the AHP software. The AHP software will automatically calculate the consistency index (CR value) using its built-in linear algebra calculation method to check the consistency of the pairwise comparison results. If the CR value is greater than 0.1, the comparison value needs to be adjusted and re-evaluated until consistency is passed. After 10 rounds of comparison, the AHP software calculates the weight of each indicator based on the importance evaluation scores given by the experts in each round, using its built-in linear algebra calculation method, and finally obtains the average weight value of each indicator.

[0078] For example, (1) the importance scores of each criterion layer and indicator layer (pairwise comparison) in the elderly family care capacity assessment index system are shown in Table 2 below.

[0079] Table 2

[0080] (2) The weight table of each evaluation standard in the assessment index system of elderly family care capacity is shown in Table 3 below.

[0081]

[0082] For example, the family-based elderly care capacity assessment method of this application is described in accordance with Tables 1, 2, and 3 and specific embodiments, as follows: Since the weighted score is calculated as: Weighted Score = Global Weight (W) × Family Original Score (X), the per capita monetary assets (A1) are: 0.5110 × 3 = 1.533; the proportion of medical expenditure (A2) is: 0.2019 × 3 = 0.6057; and the living conditions (A3) are: 0.0479 × 2 = 0.0958.

[0083] Care duration (B1): 0.1182 × 2 = 0.2364; Care skills (B2): 0.0394 × 2.8 = 0.11032 (rounded to 0.1103).

[0084] Communication and interaction (C1): 0.0408 × 4 = 0.1632; Decision participation (C2): 0.0204 × 3 = 0.0612; Conflict occurrence rate (C3): 0.0204 × 1 = 0.0204.

[0085] Overall score = 1.533 + 0.6057 + 0.0958 + 0.2364 + 0.1103 + 0.1632 + 0.0612 + 0.0204 = 2.826.

[0086] Ultimately, Mr. Li's family's weighted average score for family-based elder care capability was 2.826 out of 4. Converted to a percentage: (2.826 / 4) × 100 ≈ 70.7 points. This is a slightly above-average score, indicating that the family has a good foundation for elder care, but there are shortcomings in key areas.

[0087] The family-based elderly care capacity assessment method proposed in this application obtains data on the living conditions, care support, and emotional support of elderly people's families, and inputs these data into a trained target language model. Based on a pre-constructed family-based elderly care capacity assessment system, the model automatically maps the raw data to corresponding assessment indicators and calculates the quantitative scores of each indicator according to standardized scoring rules. Combined with the indicator weights determined by the analytic hierarchy process and the Delphi method, a scientific, objective, and comparable comprehensive assessment result of family-based elderly care capacity is finally generated. This method achieves automation, quantification, and intelligence in the assessment of family-based elderly care capacity, significantly improving assessment efficiency and accuracy, and providing reliable technical support for precise matching of community elderly care services, optimization of public resource allocation, and smart elderly care decision support.

[0088] Next, the family-based elderly care capacity assessment device proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0089] Figure 3 This is a block diagram of a family-based elderly care capacity assessment device according to an embodiment of this application.

[0090] like Figure 3 As shown, the family-based elderly care capacity assessment device 10 includes: an acquisition module 100, a processing module 200, and an identification module 300.

[0091] The acquisition module 100 is used to acquire living condition data, care support data, and emotional support data from the family elder care capability data. The processing module 200 is used to input the living condition data, care support data, and emotional support data into the trained target language model. The target language model calls a pre-established service evaluation system to map the living condition data, care support data, and emotional support data to pre-established evaluation indicators under the family elder care capability evaluation system, and calculates the quantitative score of the corresponding evaluation indicator based on the mapping data corresponding to each evaluation indicator. The identification module 300 is used to identify the weight corresponding to each evaluation indicator and generate the family elder care capability evaluation result based on the quantitative score of each evaluation indicator and the corresponding weight.

[0092] The family-based elderly care capacity assessment device proposed in this application acquires data on the living conditions, care support, and emotional support of elderly people's families, and inputs these data into a trained target language model. Based on a pre-constructed family-based elderly care capacity evaluation system, the model automatically maps the raw data to corresponding evaluation indicators and calculates the quantitative scores of each indicator according to standardized scoring rules. Combined with the indicator weights determined by the analytic hierarchy process and the Delphi method, a scientific, objective, and comparable comprehensive assessment result of family-based elderly care capacity is finally generated. This achieves automation, quantification, and intelligence in the assessment of family-based elderly care capacity, significantly improving assessment efficiency and accuracy, and providing reliable technical support for precise matching of community elderly care services, optimization of public resource allocation, and smart elderly care decision support.

[0093] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 301, the processor 302, and the computer program stored on the memory 301 and capable of running on the processor 302.

[0094] When the processor 302 executes the program, it implements the family-based elderly care capacity assessment method provided in the above embodiments.

[0095] Furthermore, electronic devices also include: Communication interface 303 is used for communication between memory 301 and processor 302.

[0096] The memory 301 is used to store computer programs that can run on the processor 302.

[0097] The memory 301 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0098] If the memory 301, processor 302, and communication interface 303 are implemented independently, then the communication interface 303, memory 301, and processor 302 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0099] Optionally, in a specific implementation, if the memory 301, processor 302, and communication interface 303 are integrated on a single chip, then the memory 301, processor 302, and communication interface 303 can communicate with each other through an internal interface.

[0100] Processor 302 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0101] This application also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor, implements the above-described method for assessing family-based elderly care capabilities.

[0102] This application also provides a computer program product, including a computer program or instructions, which, when executed, implement the above-described family-based elderly care capacity assessment method.

[0103] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0104] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0105] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0106] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0107] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

Claims

1. A method for assessing family-based elder care capacity, characterized in that, Includes the following steps: Acquire data on living conditions, care support, and emotional support from the family-based elderly care capacity data; The living conditions data, care support data, and emotional support data are input into the trained target language model. The target language model calls a pre-established service evaluation system to map the living conditions data, care support data, and emotional support data to pre-established evaluation indicators under the family elderly care capacity evaluation system, and calculates the quantitative score of the corresponding evaluation indicator based on the mapping data corresponding to each evaluation indicator. Identify the weight corresponding to each assessment indicator, and generate the family elder care capacity assessment result based on the quantitative score of each assessment indicator and its corresponding weight.

2. The method for assessing family-based elder care capacity according to claim 1, characterized in that, The processing method for the target language model includes: Extract at least one feature vector from the living conditions data, the care support data, and the emotional support data; Based on the semantic similarity between the feature vector and the evaluation index, the living conditions data, the care support data, and the emotional support data are mapped to the pre-established evaluation indexes under the family elder care capacity evaluation system. The quantitative score for each evaluation indicator is determined based on the mapping data corresponding to each evaluation indicator and the reference data for each evaluation indicator.

3. The method for assessing family-based elder care capacity according to claim 2, characterized in that, The step of determining the quantitative score corresponding to each evaluation indicator based on the mapping data corresponding to each evaluation indicator and the reference data of each evaluation indicator includes: The level of the evaluation indicator corresponding to the mapping data of each evaluation indicator is determined based on the mapping data corresponding to each evaluation indicator and the reference data of each evaluation indicator. The quantitative score for each evaluation indicator is determined based on the level of the evaluation indicator corresponding to the mapping data for each evaluation indicator.

4. The method for assessing family-based elder care capacity according to claim 3, characterized in that, The step of determining the quantitative score corresponding to each evaluation indicator based on the level of the evaluation indicator corresponding to the mapping data of each evaluation indicator includes: Obtain a mapping relationship table, wherein the mapping relationship table is the mapping relationship between the level of the evaluation indicator corresponding to the mapping data of each evaluation indicator and the quantitative score corresponding to each evaluation indicator; Using the levels of the evaluation indicators as indexes, the mapping relationship table is queried to determine the quantitative score corresponding to each evaluation indicator.

5. The method for assessing family-based elder care capacity according to claim 1, characterized in that, The family-based elderly care capacity evaluation system includes three criterion layers: a first criterion layer, a second criterion layer, and a third criterion layer. Each criterion layer includes multiple service indicators. The first criterion layer is the living conditions capacity layer, the second criterion layer is the care support capacity layer, and the third criterion layer is the emotional support capacity layer. The first criterion layer includes a first assessment indicator, a second assessment indicator, and a third assessment indicator. The first assessment indicator is the average monetary assets per elderly person, the second assessment indicator is the proportion of medical expenses to household consumption expenditure, and the third assessment indicator is the housing level. The second criterion layer includes a fourth assessment indicator and a fifth assessment indicator. The fourth assessment indicator is the average daily care time, and the fifth assessment indicator is care skills. The third criterion layer includes a sixth assessment indicator, a seventh assessment indicator, and an eighth assessment indicator. The sixth assessment indicator is communication and interaction, the seventh assessment indicator is decision-making participation, and the eighth assessment indicator is the conflict occurrence rate.

6. The method for assessing family-based elder care capacity according to claim 5, characterized in that, The process of identifying the weight corresponding to each evaluation indicator includes: Input any two evaluation indicators from each criterion layer of the family elder care capacity evaluation system into the expert model, and the expert model outputs the corresponding importance score; The consistency verification result of the importance score is determined. If the consistency verification result is less than or equal to a preset threshold, the weight corresponding to each evaluation indicator is calculated based on the importance score. If the consistency verification result is greater than the preset threshold, the importance score of each evaluation indicator is iteratively evaluated using an expert model until the consistency verification result is less than or equal to the preset threshold. Then, the weight corresponding to each evaluation indicator is calculated based on the current importance score.

7. A family-based elderly care capacity assessment device, characterized in that, include: The acquisition module is used to acquire data on living conditions, care support, and emotional support from the family elder care capability data. The processing module is used to input the living conditions data, care support data and emotional support data into the trained target language model. The target language model calls a pre-established service evaluation system to map the living conditions data, care support data and emotional support data to pre-established evaluation indicators under the family elderly care capacity evaluation system, and calculates the quantitative score of the corresponding evaluation indicator based on the mapping data corresponding to each evaluation indicator. The identification module is used to identify the weight corresponding to each assessment indicator and generate the family elderly care capacity assessment result based on the quantitative score of each assessment indicator and its corresponding weight.

8. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the family-based elderly care capacity assessment method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they are used to implement the family-based elderly care capacity assessment method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed, they implement the family-based elderly care capacity assessment method as described in any one of claims 1-6.

Images