Effect of Combination of Medical-Health Care-Psychological pension model
Bibliographic record
Abstract
Objective To explore a suitable service model of institutional pension by analyzing the validity of the Combination of Medical-Health Care-Psychological pension model that was developed and practiced for a long term by the clinical psychiatric team in Kunming Social Welfare Institute Welfare Hospital. Methods 80 elderly inpatients were divided into a study group and a control group. The elderly in the study group received the Combination of Medical-Health Care-Psychological pension model service, and the control group received the general medical care mode service. The quality of life, wellbeing, and satisfaction were assessed by interview and with a health status questionnaire, the Memorial University of Newfoundland Scale of Happiness (MUNSH), and life satisfaction scale. t-test was used to compare the difference of indexes between groups. Results Integrating supportive group psychological services in the pension model could enhance the positive psychological experience, deal with negative psychological experience, and improve the quality of life and satisfaction (P<0.05). The integrated model could improve the health status in the elderly by promoting the affective function, social function, physical pain, psychical energy, and mental health (P<0.05). Conclusion The Combination of Medical-Health Care-Psychological pension model is very suitable for the elderly in the institutions and has a very positive role in promoting their physical and mental health. Key words: Combination of Medical-Health Care; Combination of Medical-Health Care-Psychological pension model; Quality of life; Wellbeing; Life satisfaction
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".