Expert consensus on best evaluative practices in community-based rehabilitation
Bibliographic record
Abstract
PURPOSE: The objective of this study was to generate expert consensus on best evaluative practices for community-based rehabilitation (CBR). This consensus includes key features of the evaluation process and methods, and discussion of whether a shared framework should be used to report findings and, if so, which framework should play this role. METHOD: A Delphi study with two predefined rounds was conducted. Experts in CBR from a wide range of geographical areas and disciplinary backgrounds were recruited to complete the questionnaires. Both quantitative and qualitative analyses were performed to generate the recommendations for best practices in CBR evaluation. RESULTS: A panel of 42 experts reached consensus on 13 recommendations for best evaluative practices in CBR. In regard to the critical qualities of sound CBR evaluation processes, panellists emphasized that these processes should be inclusive, participatory, empowering and respectful of local cultures and languages. The group agreed that evaluators should consider the use of mixed methods and participatory tools, and should combine indicators from a universal list of CBR indicators with locally generated ones. The group also agreed that a common framework should guide CBR evaluations, and that this framework should be a flexible combination between the CBR Matrix and the CBR Principles. CONCLUSIONS: An expert panel reached consensus on key features of best evaluative practices in CBR. Knowledge transfer initiatives are now required to develop guidelines, tools and training opportunities to facilitate CBR program evaluations. IMPLICATIONS FOR REHABILITATION: CBR evaluation processes should strive to be inclusive, participatory, empowering and respectful of local cultures and languages. CBR evaluators should strongly consider using mixed methods, participatory tools, a combination of indicators generated with the local community and with others from a bank of CBR indicators. CBR evaluations should be situated within a shared, but flexible, framework. This shared framework could combine the CBR Matrix and the CBR Principles.
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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 | 0.547 | 0.536 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.012 | 0.027 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".