PRIMARY CARE AS A TOOL TO FIGHT VIOLENCE AGAINST WOMEN: AN INTERNATIONAL APPROACH
Bibliographic record
Abstract
Introduction: Domestic violence against women is recognized as a global public health problem, requiring strategic direction. The WHO in its global plan to address interpersonal violence stated that there are four strategic directions: strengthening health system governance, strengthening health service delivery and professional response capacity, strengthening program prevention, and improving information and evidence. The most mental health sequelae for female victims are depression, anxiety, post-traumatic stress disorder, and substance use. The role of primary care varies, starting with preventing, identifying and evaluating women, being a longitudinal care, as it is common for aggression to be repeated. The aim of this study is to assess how different countries deal with violence against women, and thus, indicate effective forms of intervention. Methodology: This is a literature review study, with a qualitative approach. A search was carried out in the PubMed database with the descriptors “Domestic and Sexual Violence against Women” and “primary care” between the years 2003 to 2020. After eligibility criteria, eleven articles were selected, all in English. Results: Studies from different countries were found. Each country with a cut of the theme related to cultural disparities. So in India it was discussed how loveless marriages arranged by families can be related to domestic violence. In Canada, there is a study of ways to approach women victims of intimate partner violence. Some people argued that health professionals should ask women about domestic violence. They argue that ‘screening’ can encourage revealing abuse or acknowledging their own experience as violence. Others argue that such screening should be targeted at high-risk groups. Studies have suggested that women have a preference for screening methods that do not involve health professionals, given the sensitive nature of domestic violence. Personalized online interactions are currently being studied to help women in situations of violence. These can improve access to information and support for safety and well-being planning, particularly for women who are reluctant. Conclusion: It is necessary to train professionals to identify and approach violence against women, in addition, it is necessary to formulate well-defined public policies that guarantee comprehensive care. Increasing screening sensitivity, appropriate treatment for physical and psychological sequelae.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".