Understanding key drivers of performance in the provision of maternal health services in eastern cape, South Africa: a systems analysis using group model building
Bibliographic record
Abstract
BACKGROUND: The Eastern Cape Province reports among the poorest health service indicators in South Africa with some of its districts standing out as worst performing as regards maternal health indicators. To understand key drivers and outcomes of this underperformance and to explore whether a participatory analysis could deepen action-oriented understanding among stakeholders, a study was conducted in one of the chronically poorly performing districts. METHODS: The study used a systems analysis approach to understand the drivers and outcomes affecting maternal health in the district in order to identify key leverage points for addressing the situation. The approach included semi-structured interviews with a total of 24 individuals consisting health system managers at various levels, health facility staff and patients. This was followed by a participatory group model building exercise with 23 key stakeholders to analyze system factors and their interrelationships affecting maternal health in the district using rich pictures and interrelationship diagraphs (IRDs) and finally the development of causal loop diagrams (CLDs). RESULTS: The stakeholders were able to unpack the complex ways in which factors were interrelated in contributing to poor maternal health performance and identified the feedback loops which resulted in the situation being intractable, suggesting strategies for sustainable improvement. Quality of leadership was shown to have a pervasive influence on overall system performance by linking to numerous factors and feedback loops, including staff motivation and capacity building. Staff motivation was linked to quality of care in turn influencing patient attendance and feeding back into staff motivation through its impact on workload. Without attention to workload, patient waiting times and satisfaction, the impact of improved leadership and staff support on staff competence and attitudes would be diminished. CONCLUSION: Understanding the complex interrelationships of factors in the health system is key to identifying workable solutions especially in the context of chronic health systems challenges. Systems modelling using group model building methods can be an efficient means of supporting stakeholders to recognize valuable resources within the context of a dysfunctional system to strengthen systems performance.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".