Ethics and Community-Based Rehabilitation: Eight Ethical Questions from a Review of the Literature
Bibliographic record
Abstract
Purpose: This article reviews the literature regarding ethics and community-based rehabilitation (CBR) with the goal of identifying and analyzing ethical considerations associated with this approach. Method: We conducted a critical interpretive review of the academic literature related to CBR in low- and middle-income countries and to indigenous communities in high-income countries. Using an inductive analysis of the collected articles, we identified five key topic areas related to ethical considerations. We then critically appraised this literature and developed eight questions that reflect areas of ethical tension, uncertainty, or debate. Results: The five key topic areas are partnerships among stakeholders, respect for culture and local experience, empowerment, accountability, and fairness in programme design. The eight ethical questions are linked to these topics and associated with how CBR practices reflect commitments to equity, respect, inclusion, participation, and social justice. Conclusion: Continued engagement with ethical considerations associated with CBR can help to strengthen the foundations of this important and influential approach. It is crucial that all those involved in CBR projects, including physiotherapists, pay careful attention to the development of partnerships that, despite asymmetries among stakeholders, are respectful and effective.
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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 | 0.054 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| 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".