Health promotion capacity and institutional systems: an assessment of the South African Department of Health
Bibliographic record
Abstract
Health promotion (HP) capacity of staff and institutions is critical for health-promoting programmes to address social determinants of health and effectively contribute to disease prevention. HP capacity mapping initiatives are the first step to identify gaps to guide capacity strengthening and inform resource allocation. In low-and-middle-income countries, there is limited evidence on HP capacity. We assessed collective and institutional capacity to prioritize, plan, deliver, monitor and evaluate HP within the South African Department of Health (DoH). A concurrent mixed methods study that drew on data collected using a participatory HP capacity assessment tool. We held five 1-day workshops (one national, two provincial and two districts) with DoH staff (n = 28). Participants completed self-assessments of collective capacity across three areas: technical, coordinating and systems capacity using a four-point Likert scale. HP capacity scores were analysed and presented as means with standard deviations (SDs). Thematic analysis of verbatim transcripts of audio-recorded group discussions that provided rationale and evidence for scores were conducted using deductive and inductive codes. At all levels, groups revealed that capacity to develop long-term, sustainable HP interventions was limited. We found limited collaboration between national and provincial HP levels. There was limited monitoring of HP indicators in the health information system. Coordination of HP efforts across different sectors was largely absent. Lack of capacity in budgeting emerged as a major challenge, with few resources available to conduct HP activities at any level. Overall, the capacity mean score was 2.08/4.00 (SD = 0.83). There is need to overcome institutional barriers, and strengthen capacity for HP implementation, support and evaluation within the South African DoH.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".