User fee policies and women’s empowerment: a systematic scoping review
Bibliographic record
Abstract
BACKGROUND: Over the past decade, an increasing number of low- and middle-income countries have reduced or removed user fees for pregnant women and/or children under five as a strategy to achieve universal health coverage. Despite the large number of studies (including meta-analyses and systematic reviews) that have shown this strategy's positive effects impact on health-related indicators, the repercussions on women's empowerment or gender equality has been overlooked in the literature. The aim of this study is to systematically review the evidence on the association between user fee policies in low- and middle-income countries and women's empowerment. METHODS: A systematic scoping review was conducted. Two reviewers conducted the database search in six health-focused databases (Pubmed, CAB Abstracts, Embase, Medline, Global Health, EBM Reviews) using English key words. The database search was conducted on February 20, 2020, with no publication date limitation. Qualitative analysis of the included articles was conducted using a thematic analysis approach. The material was organized based on the Gender at Work analytical framework. RESULTS: Out of the 206 initial records, nine articles were included in the review. The study settings include three low-income countries (Burkina Faso, Mali, Sierra Leone) and two lower-middle countries (Kenya, India). Four of them examine a direct association between user fee policies and women's empowerment, while the others address this issue indirectly -mostly by examining gender equality or women's decision-making in the context of free healthcare. The evidence suggests that user fee removal contributes to improving women's capability to make health decisions through different mechanisms, but that the impact is limited. In the context of free healthcare, women's healthcare decision-making power remains undermined because of social norms that are prevalent in the household, the community and the healthcare centers. In addition, women continue to endure limited access to and control over resources (mainly education, information and economic resources). CONCLUSION: User fee removal policies alone are not enough to improve women's healthcare decision-making power. Comprehensive and multi-sectoral approaches are needed to bring sustainable change regarding women's empowerment. A focus on "gender equitable access to healthcare" is needed to reconcile women's empowerment and the efforts to achieve universal health coverage.
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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 | 0.027 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".