The policy transfer of community-based rehabilitation in Gulu, Uganda
Bibliographic record
Abstract
This paper explores the influence of the community-based rehabilitation approach among international development actors in Gulu, Uganda through the lens of policy transfer. Developed by international organizations, this approach has been promoted as a meaningful way to address the needs of persons with disabilities in low-income countries. This qualitative case study consists of a questionnaire completed by representatives from 25 different organizations, in addition to semi-structured follow-up interviews with 8 development professionals. The findings indicate that the community-based rehabilitation approach has had some influence, as respondents appeared cognisant of key principles of the approach in relation to persons with disabilities. However, detailed knowledge of the approach is mainly limited to the field of health, and there were few current examples of the approach being implemented. These results illustrate the challenges of implementing community-based rehabilitation specifically, as well as broader issues related to the transfer of international policy ideas to the Global South.Points of interestCommunity-based rehabilitation is a way of helping people with disabilities in low-income countries where services are limitedDevelopment organizations in Gulu, Uganda (like the local government and non-governmental organizations) think that this approach is a good way to support people with disabilitiesOnly a few of the people interviewed for this study had a strong understanding of the community-based rehabilitation approach, and these people mostly worked in the health sectorThere were no examples of community-based rehabilitation programs in Gulu at the time of the study, but some projects (mostly in the health field) used some ideas drawn from the approachLocal development organizations find it difficult to apply policy ideas like community-based rehabilitation, especially when they do not have consistent funding
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".