Rights-Based Training Enhancing Engagement of Health Providers With Communities, Cape Metropole, South Africa
Bibliographic record
Abstract
Community participation, the central principle of the primary health care approach, is widely accepted in the governance of health systems. Health Committees (HCs) are community-based structures that can enable communities to participate in the governance of primary health care. Previous research done in the Cape Town Metropole, South Africa, reports that HCs' potential can, however, be limited by a lack of local health providers' (HPs) understanding of HC roles and functions as well as lack of engagement with HCs. This study was the first to evaluate HPs' responsiveness towards HCs following participation in an interactive rights-based training. Thirty-four HPs, from all Cape Metropole health sub-districts, participated in this qualitative training evaluation. Two training groups were observed and participants completed pre- and post-training questionnaires. Semi-structured interviews were held with 10 participants 3-4 months after training. Following training, HPs understood HCs to play an important role in the communication between the local community and HPs. HPs also perceived HCs as able to assist with and improve the quality and accessibility of PHC, as well as the answerability of services to local community needs. HPs expressed intentions to actively engage with the facility's HC and stressed the importance of setting clear roles and responsibilities for all HC members. This training evaluation reveals HPs' willingness to engage with HCs and their desire for skills to achieve this. Moreover, it confirms that HPs are crucial players for the effective functioning of HCs. This evaluation indicates that HPs' increased responsiveness to HCs following training can contribute to tackling the disconnect between service delivery and community needs. Therefore, the training of HPs on HCs potentially promotes the development of needs-responsive PHC and a people-centred health system. The training requires ongoing evaluation as it is extended to other contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".