Latin American and Caribbean countries’ baseline clinical and policy guidelines for responding to intimate partner violence and sexual violence against women
Bibliographic record
Abstract
BACKGROUND: Violence against women is a global public health problem with negative effects on physical, mental, and reproductive health. The World Health Organization (WHO) has identified intimate partner violence (IPV) and sexual violence (SV) as major targets for prevention and amelioration and recently developed clinical and policy guidelines to assist healthcare providers. This project was undertaken to determine the 2013 baseline national policies and clinical guidelines on IPV and SV within the Latin American and Caribbean (LAC) region to identify strengths and gaps requiring action. METHODS: Each Pan American Health Organization/World Health Organization Regional Office for the Americas (PAHO/WHO) country focal point was contacted to request their current national policy and clinical guidelines (protocol) on IPV/SV. We augmented this by searching the internet and the United Nations Women website. Each country's policy and clinical guideline (where available) was reviewed and entered into a scoring matrix based on WHO Clinical and Policy Guidelines. A total score for each heading and subheading was developed by adding positive responses to identify LAC regional strengths and gaps. RESULTS: We obtained 15 national policies and 12 national clinical guidelines (protocols) from a total of 18 countries ("response" rate 66.7%). National policies were comprehensive in terms of physical, emotional, and sexual violence and recommended good intersectoral collaboration. The greatest gap was in the training of health-care providers. National Guidelines for women-centered care for IPV/SV survivors were strong in the vital areas of privacy, confidentiality, danger assessment, safety planning, and supportive reactions to disclosure. The largest gaps noted were again in training healthcare professionals and strengthening monitoring and evaluation of services. CONCLUSIONS: Baseline measurement of policy and clinical guidelines for IPV/SV in LAC PAHO/WHO member countries at the time of issuing the 2013 WHO Clinical and Policy Guidelines reveals some important strengths, but also serious gaps that need to be addressed. The most pressing needs are for concerted training initiatives for healthcare providers and strengthening multisectoral monitoring and evaluation of services. A future evaluation of national policies, clinical guidelines, monitoring and evaluation will need to be conducted to measure the progress of the required scaling-up process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".