South African stakeholders’ knowledge of community-based rehabilitation
Bibliographic record
Abstract
BACKGROUND: Community-based rehabilitation (CBR) is a complex concept and strategy that has been implemented in diverse ways globally and in South Africa. Internationally, some stakeholders have described CBR as confusing, and this may influence implementation. A southern African study reports that there is insufficient evidence of the understanding of CBR in the region to influence training, policy and practice. OBJECTIVES: The aim of this study was to investigate South African stakeholders' knowledge of CBR. METHOD: This article reports on an electronic survey that was part of a larger mixed methods study. Based on the sample of 86 respondents, descriptive statistics were used to analyse the quantitative data and thematic analysis for the qualitative data. RESULTS: The majority of respondents had had exposure to CBR, but almost a quarter had no knowledge of the CBR guidelines and matrix. The results revealed varying knowledge concerning the key concepts of CBR, its beneficiaries and its funders. Respondents identified persons with disabilities as having a central role in the implementation of CBR. Problems with the visibility of CBR programmes were noted, as well as misunderstandings by many therapists. CONCLUSION: The implementation of CBR, and its goal of ensuring the rights of persons with disabilities, is negatively affected by the confusion attached to the understanding of what CBR is. The misunderstandings about, and lack of visibility of, CBR in South Africa may hinder its growing implementation in the country in line with new government policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".