Priority setting for maternal, newborn and child health in Uganda: a qualitative study evaluating actual practice
Bibliographic record
Abstract
BACKGROUND: Despite continued investment, Maternal, Newborn and Child Health (MNCH) indicators in low and middle income countries have remained relatively poor. This could, in part, be explained by inadequate resources to adequately address these problems, inappropriate allocation of the available resources, or lack of implementation of the most effective interventions. Systematic priority setting and resource allocation could contribute to alleviating these limitations. There is a paucity of literature that follows through MNCH prioritization processes to implementation, making it difficult for policy makers to understand the impact of their decision-making on population health. The overall objective of this paper was to describe and evaluate priority setting for maternal, newborn and child health interventions in Uganda. METHODS: Fifty-four key informant interviews and a review of policies and media reports were used to describe priority setting for MNCH in Uganda. Kapiriri and Martin's conceptual framework was used to evaluate priority setting for MNCH. RESULTS: There were three main prioritization exercises for maternal, newborn and child health in Uganda. The processes were participatory and were guided by explicit tools, evidence, and criteria, however, the public and the districts were insufficiently involved in the priority setting process. While there were conducive contextual factors including strong political support, implementation was constrained by the presence of competing actors, with varying priorities, an unequal allocation of resources between child health and maternal health interventions, limited financial and human resources, a weak health system and limited institutional capacity. CONCLUSIONS: Stronger institutional capacity at the Ministry of Health and equitable engagement of key stakeholders in decision-making processes, especially the public, and implementers, would improve understanding, satisfaction and compliance with the priority setting process. Availability of financial and human resources that are appropriately allocated would facilitate the implementation of well-developed policies.
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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 | 0.040 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".