Women’s empowerment under the lens of global health equity: Literature review of challenges, best practices, and societal impact
Bibliographic record
Abstract
Introduction: Women’s empowerment refers to strengthening the social, economic, and educational powers of women. Numerous global initiatives have been implemented to enable women to make decisions about their health and lives. The purpose of this literature review was to review women’s health barriers, related best practices, and their impacts on women's empowerment in the global context. Methods: We comprehensively searched databases: PubMed, CINAHL, and Google Scholar and key program websites. To expand our review, we used a snowball strategy to scan articles, documents, reports, and research outputs of global programs and best practices to overcome barriers. Also, we screened publications posted on websites of organizations working on women’s empowerment and monitoring the Sustainable Development Goals progress. Results: Findings suggest significant advancements have been made in the status of women's health over the past two decades. However, the COVID-19 pandemic has aggravated health barriers, impeding, and even reversing progress, which has resulted in widening women’s inequity in access to health care services worldwide. Conclusions: Findings suggest that successful and sustainable programs should have a multisectoral, multilevel and multistakeholder, diversified interventions approach with social empowerment, as well as the need for legislative enforcement against discrimination and harmful practices towards women.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".