Obstacles to advancing women’s health in Mozambique: a qualitative investigation into the perspectives of policy makers
Bibliographic record
Abstract
BACKGROUND: Despite substantial investment in women's health over the past two decades, and enthusiastic government support for MDG 5 and SDG 3, health indicators for women in Mozambique remain among the lowest in the world. Maternal mortality stayed constant from 2003 to 2011, with an MMR of 408; the estimated HIV prevalence for women of 15-24 years is over twice that for men; and only 12.1% of women are estimated to be using modern contraception. This study explores the perspectives of policy makers in the Mozambican health system and affiliates on the challenges that are preventing Mozambique from achieving greater gains in women's health. METHODS: We conducted in-depth interviews with 39 senior- and mid-level policy makers in the Ministry of Health and affiliated institutions (32 women, 7 men). Participants were sampled using a combination of systematic random sampling and snowball sampling. Participants were asked about their experiences formulating and implementing health policies and programs, what is needed to improve women's health in Mozambique, and the barriers and opportunities to achieving such improvement. RESULTS: Participants unanimously argued that women's health is already sufficiently prioritized in national health policies and strategies in Mozambique; the problem, rather, is the implementation and execution of existing women's health policies and programs. Participants raised challenges related to the policy making process itself, including an ever-changing, fragmented decision-making process, lack of long-term perspective, weak evaluation, and misalignment of programs across sectors. The disproportionate influence of donors was also mentioned, with lack of ownership, rapid transitions, and vertical programming limiting the scope for meaningful change. Finally, participants reported a disconnect between policy makers at the national level and realities on the ground, with poor dissemination of strategies, limited district resources, and poor consideration of local cultural contexts. CONCLUSIONS: To achieve meaningful gains in women's health in Mozambique, more focus must be placed on resolving the bottleneck that is the implementation of existing policies. Barriers to implementation exist across multiple health systems components, therefore, solutions to address them must also reach across these multiple components. A holistic approach to strengthening the health system across multiple sectors and at multiple levels is needed. SUPPLEMENTARY INFORMATION: accompanies this paper at (10.1186/s41256-019-0119-x).
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".