Long-Term Care Needs-based Hierarchical Recipients Model

BackgroundWith the growth of the aging population, the change of disease pattern and the decrease of the traditional home care, a scientific, standard and sustainable long-term care system is of tremendous importance, necessity and urgency. However, the establishment of this long-term care imposes problems to the policy-makers. That is how to objectively and effectively assess whether the elders need long-term care and which class of service they need. assessment of long-term care needs can solve these issues.ObjectiveThis research aims to build a Long-Term Care Needs-Based Hierarchical Recipients Model, in order to provide technical support for the establishment of the long-term care and security system.MethodologyIn the preliminary research of the project--Long Term Care Nursery-Needs-Based Hierarchical Recipients Model--sponsored by National Natural Science Foundation, we have already finished the design of the Long-Term Care Assessment Form, which has been widely favored and confirmed. Our research is based on the Long-Term Care Assessment Instrument and2893sets of investigation data from the preliminary research.We take expert classification as golden criteria for selecting variables and building the model. We invited four experts, who have been working in the files for long time, to classify the recipients into third class, second class, first class and professional class, based on the Long-Term Care Assessment standard and their experience. Each Long-Term Care Assessment Form is assessed by two experts in order to maintain the accuracy. If the result is different between these two experts, those forms will be collected and discussed for final classification by all the experts.The core variables are determined by expert consulting, statistical analysis and literature research. Those who were involved in determining core variables include five long-term care experts, three long-term care administrative staff, four researchers in long-term care and two original designers of these variables. The preliminary selection consists of two rounds while the final selection includes three rounds. Statistical analysis consists of rank correlation test and cluster analysis.In building the model, the weights and percentile of those variables are determined by experts consulting, while the cut-off value for dividing different classes are based on the results of experts classification.In the empirical tests of the model, we did consistency test between our model and the results of experts classification. And, we compared the classification results between different groups to check the sensitivity and effectiveness of the model.ResultsWe finally selected16variables into model. And, we determined three cut-off values to divide recipients into four needs classes—normal, slight, moderate and severe. Under the ambiguity of differentiating between first class and professional class, we consider the effectiveness of the model as good if the results of the consistency test are greater than70.06%. The classification of different recipients has obvious difference and the difference is consistent with the facts, indicating the great sensitivity and effectiveness of the model.DiscussionThe unsatisfactory parts of this research, is that we did not compare the model with the model in other countries. Such comparison is impractical and not very meaningful since the standards are rooted in different backgrounds and different countries do not have a common principle. Our research is based on empirical tests, regarding the expert classification as the most appropriate reference. Meanwhile, we referred a lot of international experience in building the model, which can make up the defects.For the application of the model, the model is intended to apply into the old people who have potential long-term care needs, into classifying the long-term care needs of the old people, and into long-term care service delivery system and security system.The further directions of the research:1) further evaluation of the model and improving the model;2) apply the model and Long-Term Care Assessment Form into the measurement and prediction of the needs of the long-term care in Shanghai.