Gender-Based Violence (GBV) and the impact of violence on health and the role of trauma.
Bibliographic record
Abstract
Gender-based violence (GBV) is a growing and pressing public health issue. Intimate partner violence (IPV), one kind of GBV, has been declared an epidemic locally in Windsor-Essex County, joining 96 other municipalities across Ontario who have recognized the severity of this concern. We conducted a scoping review (SR) to better understand the impact that trauma resulting from violence has on health outcomes of GBV survivors. Our SR serves to conceptualize the discussion of trauma as it relates to health and GBV in research contexts. Empirical research investigating trauma and health outcomes resulting from violence is lacking, which our SR seeks to address through improving the knowledge base and conceptualization of these topics from the literature. The literature search was completed in September 2024, where the key information sources included six academic databases (e.g. MEDLINE via Ovid, Scopus, etc.). Solely peer-reviewed studies published since 2010, that discuss GBV, women's health, and trauma-related outcomes were included. Extracted data was tabulated and accompanied with descriptive qualitative data analysis. Emerging themes include but are not limited to: the role of trauma and violence being cyclical, various health consequences (e.g., reproductive health challenges, chronic pain, substance use, etc.), how survivors cope with adverse violence-induced outcomes. Findings will help inform policy and practice implications, such as a need for accessible mental health and medical care, trauma and violence informed care, and sensitivity training about women's lived experiences. Our SR emphasizes that health care professionals and organizations supporting survivors must be responsive to survivor's needs.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".