Education Based on Precede-Proceed on Quality of Life in Elderly
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: One of the most important challenges in public health is to improve the quality of life in elders. Aging may cause various disorders such as disabilities, high risk conditions and some chronic disease. In this study the effect of educational intervention based on precede-proceed on quality of life in elders was examined. MATERIALS & METHODS: This semi experimental study was carried out on 128 elders over 60 years in Zahedan that were randomly selected by multi-stage sampling method and divided in to control and intervention groups. Data collection tool was a triploid questionnaire that included demographic data, questions of precede-proceed constructs and SF-36 questionnaire. The validity and reliability of questionnaire confirmed by experts and Cranach's Alpha coefficient (76%). After primary data collecting, educational intervention was performed and after nine months data was collected again and analyzed in spss.16 soft-ware using descriptive and analytical statistics. RESULTS: The results showed that mean score of quality of life in participants was low and more than 61% of them had a mean score less than 50%. After intervention the mean score of quality of life only in experimental group significantly increased from 47.72 to 58.90. Behavior and self-rated health were the strongest predictors for quality of life in this study. CONCLUSION: Implementation educational intervention based on precedes-proceed model can improve quality of life in elders. Elderly women and older elderly individuals compared with elderly men and younger elderly should be considering as an important risk factor for reducing HRQOL.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".