Exploring how different modes of governance act across health system levels to influence primary healthcare facility managers’ use of information in decision-making: experience from Cape Town, South Africa
Bibliographic record
Abstract
BACKGROUND: Governance, which includes decision-making at all levels of the health system, and information have been identified as key, interacting levers of health system strengthening. However there is an extensive literature detailing the challenges of supporting health managers to use formal information from health information systems (HISs) in their decision-making. While health information needs differ across levels of the health system there has been surprisingly little empirical work considering what information is actually used by primary healthcare facility managers in managing, and making decisions about, service delivery. This paper, therefore, specifically examines experience from Cape Town, South Africa, asking the question: How is primary healthcare facility managers' use of information for decision-making influenced by governance across levels of the health system? The research is novel in that it both explores what information these facility managers actually use in decision-making, and considers how wider governance processes influence this information use. METHODS: An academic researcher and four facility managers worked as co-researchers in a multi-case study in which three areas of management were served as the cases. There were iterative cycles of data collection and collaborative analysis with individual and peer reflective learning over a period of three years. RESULTS: Central governance shaped what information and knowledge was valued - and, therefore, generated and used at lower system levels. The central level valued formal health information generated in the district-based HIS which therefore attracted management attention across the levels of the health system in terms of design, funding and implementation. This information was useful in the top-down practices of planning and management of the public health system. However, in facilities at the frontline of service delivery, there was a strong requirement for local, disaggregated information and experiential knowledge to make locally-appropriate and responsive decisions, and to perform the people management tasks required. Despite central level influences, modes of governance operating at the subdistrict level had influence over what information was valued, generated and used locally. CONCLUSIONS: Strengthening local level managers' ability to create enabling environments is an important leverage point in supporting informed local decision-making, and, in turn, translating national policies and priorities, including equity goals, into appropriate service delivery practices.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".