A mixed‐methods assessment of sexual and gender‐based violence in eastern Democratic Republic of Congo to inform national and international strategy implementation
Bibliographic record
Abstract
CONTEXT: National and international strategies were implemented in eastern Democratic Republic of Congo (DRC) to address sexual and gender-based violence (SGBV). OBJECTIVES: The objective was to assess community attitudes of SGBV and health facility capacity to address SGBV in eastern DRC. DESIGN AND SETTING: The design and setting are as follows: a cross-sectional, population-based cluster survey of 998 adults in eastern DRC territories, a convenience sample of 27 adults using semi-structured directed interviews, qualitative data from 37 focus groups conducted in three health zones, assessment of 64 health facilities and a comparative analysis of SGBV strategies. MAIN OUTCOME MEASURES: The main outcome measures opinions regarding SGBV prevention and justice and health facility capacity to address SGBV. RESULTS: The majority of respondents favored the legal system over community mediation to obtain justice for SGBV. However, 61.1% (95% CI, 51.8-70.5%) of SGBV survivors reported being forced to accept community mediation. Among SGBV survivors, 81.2% (95% CI, 74.5-87.8%) reported no available mental health care. Less than half of all respondents reported access to a hospital, clinic or pharmacy. The analyses and facility assessment reinforce the need to improve SGBV care. CONCLUSIONS: Mixed methodologies point to the complexities of addressing SGBV, assess key elements of SGBV prevention, justice and response, and may ultimately inform national and international strategies.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".