Managers’ perceptions on the implementation of community-based rehabilitation in KwaZulu-Natal
Bibliographic record
Abstract
Background: Community-based rehabilitation (CBR) is a World Health Organization (WHO) strategy for social inclusion, equalisation of opportunities and provision of essential services for people with disabilities (PWDs). Community-based rehabilitation is a multi-sectoral strategy that requires all stakeholders to participate equally in its implementation. KwaZulu-Natal has implemented CBR for over two decades, with various stakeholders at the forefront of implementation. However, the status of stakeholder engagement, collaboration and coordination is unknown. Objective: The objective of our study was to understand how CBR is implemented in KwaZulu-Natal and the roles of each stakeholder in its implementation, with a focus, on managers from government and non-governmental organisations. Method: A descriptive explorative approach using semi-structured interviews was used to collect data from 20 managers from various stakeholders involved in implementing CBR in KwaZulu-Natal. Data were transcribed and analysed using thematic analysis. Results: The findings revealed five dominant themes: (1) the understanding of concepts, (2) missed opportunities for implementing CBR, (3) barriers to implementing CBR, (4) benefits to implementing CBR and (5) recommendations for future implementation. Conclusion: A formal management structure with clear roles and responsibilities was fundamental for implementation. Collaboration, coordination and planning were believed to be the critical roles of managers in the implementation of CBR. Training, awareness and sharing of resources among stakeholders were also identified as important factors in implementing CBR in KwaZulu-Natal. Clinical implications: Our study will assist managers and clinicians to improve their planning and implementation of CBR.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".