A framework for evaluating community-based rehabilitation programmes in Chinese communities
Bibliographic record
Abstract
PURPOSE: The primary aim of this study was to develop an evaluation framework that could effectively describe the quality of community-based rehabilitation (CBR) practice in Chinese communities. METHOD: This study adopted a case study approach to build and validate a CBR evaluation framework. Core elements of CBR programmes were defined from the literature to form an Initial Framework. Domains and elements of the Initial Framework were then verified with examples of CBR programmes cited in published articles. The revised framework was then further tested for relevance and appropriateness in the real life context through testing in five Chinese CBR programmes. RESULTS: A final framework for evaluating CBR programmes was developed. It consists of 5 domains, 25 categorised core elements and 72 indicators. CONCLUSION: A comprehensive CBR evaluation framework was built and initially verified with domains, elements and indicators, and is ready for use in Chinese CBR settings.
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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 | 0.061 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".