Designed to Fail? Revisiting Uganda’s Maternal Health Policies to Understand Policy Design Issues Underpinning Missed Targets for Reduction of Maternal Mortality Ratio (MMR): 2000-2015
Bibliographic record
Abstract
BACKGROUND: Despite Uganda and other sub-Saharan African countries missing their maternal mortality ratio (MMR) targets for Millennium Development Goal (MDG) 5, limited attention has been paid to policy design in the literature examining the persistence of preventable maternal mortality. This study examined the specific policy interventions designed to reduce maternal deaths in Uganda and identified particular policy design issues that underpinned MDG 5 performance. We suggest a novel prescriptive and analytical (re)conceptualization of policy in terms of its fidelity to '3Cs' (coherence of design, comprehensiveness of coverage and consistency in application) that could have implications for future healthcare programming. METHODS: We conducted a retrospective study. Sixteen Ugandan maternal health policy documents and 21 national programme performance reports were examined, and six key informant interviews conducted with national stakeholders managing maternal health programmes during the reference period 2000-2015. We applied the analytical framework of the 'three delay model' combined with a broader literature on 'policy mixing.' RESULTS: Despite introducing fourteen separate policy instruments over 15 years with the goal of reducing maternal mortality, by the end of the MDG period in 2015, only 87.5% of the interventions for the three delays were covered with a notable lack of coherence and consistency evident among the instruments. The three delays persisted at the frontline with 70% of deaths by 2014 attributed to failures in referral policies while 67% of maternal deaths were due to inadequacies in healthcare facilities and trained personnel in the same period. By 2015, 37.3% of deaths were due to transportation issues. CONCLUSION: The piecemeal introduction of additional policy instruments frequently distorted existing synergies among policies resulting in persistence of the three delays and missed MDG 5 target. Future policy reforms should address the 'three delays' but also ensure fidelity of policy design to coherence, comprehensiveness and consistency.
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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 | 0.060 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".