The Health Sector Response to Gender-Based Violence and Sexual Reproductive Health Programs in the Commonwealth and Selected African Countries: Protocol for a Mixed Methods Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The intertwining nature of gender-based violence (GBV) and violence perpetrated against women and girls (VAWG), as well as sexual and reproductive health rights (SRHR), underlines the urgent need for the health sector to enhance the coordination of services to improve health outcomes. Importantly, GBV and VAWG are intricately linked to a spectrum of SRHR challenges, ranging from unintended pregnancies to severe maternal, gynecological, and mental health outcomes. Cumulative GBV had a more significant effect on abortion risk than associated variables. Recognizing the interplay between GBV, VAWG, and SRHR highlights the necessity for a comprehensive health sector response. A systematic review of the health sector response to GBV, VAWG, and SRHR will be conducted to understand the extent and array of health facility-based coordinated responses to GBV, VAWG, and SRHR; lessons learned; and successes and challenges in the Commonwealth and selected African countries. OBJECTIVE: We aim to understand the context of GBV, VAWG, and SRHR by conducting a comprehensive review of health sector responses in different national, cultural, and socioeconomic contexts, and we aim to share best practices, experiences, and lessons learned. METHODS: A mixed methods systematic review will be conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA-P) guidelines. The population, intervention, comparison, and outcome framework will be applied to screen and select relevant sources guided by the inclusion and exclusion criteria. The review will include relevant research papers published in the last 15 years and conducted in the 24 Commonwealth and 7 selected African countries. Electronic databases to be searched will include PubMed, Google Scholar, Science Direct, EBSCOhost, Web of Science, Embase, PsycINFO, Cochrane, CINAHL, Index Medicus for the Eastern Mediterranean Region, and POPline. RESULTS: Ethics approval will be waived as the study will use data in the public domain. The project has been commissioned by the Commonwealth Secretariat (2022-2025). The database search, data screening, and data extraction process for the review will be completed by September 2025. A manuscript will be submitted to a peer-reviewed international journal by November 2025. The initial online database searches, citations of eligible studies, and Microsoft Copilot identified 38,200 studies focusing on GBV, VAWG, and SRHR interventions. To date, 60 studies have been found eligible for inclusion in the review. The majority of these studies were conducted in eastern Africa (n=34), South Africa (n=14), and Asia (n=13). Evidence generated from this review will be made available through journal publications, seminars and workshops with key stakeholders, ministries of health, and local and international conferences. CONCLUSIONS: The study will generate evidence to inform recommendations on addressing and mitigating the effects of GBV and VAWG on SRHR outcomes and coordinated services in the health sectors of Commonwealth and selected African countries. TRIAL REGISTRATION: PROSPERO CRD42024520594; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024520594. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/67571.
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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 | 0.140 | 0.170 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.023 | 0.034 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.052 | 0.006 |
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".