A systematic review on how to conduct evaluations in community-based rehabilitation
Bibliographic record
Abstract
PURPOSE: Community-based rehabilitation (CBR) must prove that it is making a significant difference for people with disabilities in low- and middle-income countries. Yet, evaluation is not a common practice and the evidence for its effectiveness is fragmented and largely insufficient. The objective of this article was to review the literature on best practices in program evaluation in CBR in relation to the evaluative process, the frameworks, and the methods of data collection. METHOD: A systematic search was conducted on five rehabilitation databases and the World Health Organization website with keywords associated with CBR and program evaluation. Two independent researchers selected the articles. RESULTS: Twenty-two documents were included. The results suggest that (1) the evaluative process needs to be conducted in close collaboration with the local community, including people with disabilities, and to be followed by sharing the findings and taking actions, (2) many frameworks have been proposed to evaluate CBR but no agreement has been reached, and (3) qualitative methodologies have dominated the scene in CBR so far, but their combination with quantitative methods has a lot of potential to better capture the effectiveness of this strategy. CONCLUSIONS: In order to facilitate and improve evaluations in CBR, there is an urgent need to agree on a common framework, such as the CBR matrix, and to develop best practice guidelines based on the literature available and consensus among a group of experts. These will need to demonstrate a good balance between community development and standards for effective evaluations. Implications for Rehabilitation In the quest for evidence of the effectiveness of community-based rehabilitation (CBR), a shared program evaluation framework would better enable the combination of findings from different studies. The evaluation of CBR programs should always include sharing findings and taking action for the sake of the local community. Although qualitative methodologies have dominated the scene in CBR and remain highly relevant, there is also a call for the inclusion of quantitative indicators in order to capture the progress made by people participating in CBR programs. The production of best practice guidelines for evaluation in CBR could foster accountable and empowering program evaluations that are congruent with the principles at the heart of CBR and the standards for effective evaluations.
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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 | 0.198 | 0.421 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.031 | 0.021 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".