The gender-based violence and recovery centre at Coast Provincial General Hospital, Mombasa, Kenya: An integrated care model for survivors of sexual violence
Bibliographic record
Abstract
points• Sexual violence (SV) is highly prevalent and a major public health problem globally.In Kenya, an estimated 32% of females and 18% of males were reported to have experienced SV before the age of 18 years.• This paper presents a data set collected between 2007 and 2018 and describes the gender-based violence and recovery centre (GBVRC) model under which survivors of SV were cared for at a 24-hour public hospital in Mombasa, Kenya-including its development, implementation, achievements, and challenges.• The GBVRC model is a partnership that provides (in addition to emergency healthcare) mental health support, paralegal services, and integrated cooperation with police, judiciary, local leaders, and the wider community.The Mombasa GBVRC has provided post-SV care to 6,575 people reporting SV, of whom 88% were female and over 50% were younger than 16 years.Over 90% of the perpetrators were family, neighbours, community members, or in some other way known to the survivors.• The low rate (19%) of attendance by survivors for the second counselling visit suggests a more robust strategy is needed for follow-up-for example, by referring people back to smaller, closer health facilities.A second limitation was a lack of trained staff, although this is an expected issue in sub-Saharan Africa.There was also a low rate of legal resolution to the cases.This may be due to the need for education about the standard of evidence required by courts.• The experiences of successful and sustainable implementation of the GBVRC model should strengthen arguments for service delivery for people experiencing SV in this and similar settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".