Exploring the intersection of traumatic brain injury and mental health in survivors of intimate partner violence
Bibliographic record
Abstract
Introduction: One in four Canadian women experience intimate partner violence (IPV) in their lifetime. The COVID-19 pandemic has significantly increased rates of IPV globally and the level of violence encountered, exposing IPV survivors to greater risk of physical injury, including traumatic brain injury (TBI). Up to 75% of survivors are suspected of sustaining a TBI and 50-75% experience mental health or substance use challenges (MHSU) as a result of IPV, resulting in extensive personal, social, and economic implications. Objective: The objective of this scoping review was to synthesize what is currently known in the literature about MHSU and TBI among survivors of IPV and identify gaps. Methods: MEDLINE, EMBASE, PsycINFO, CINAHL, Cochrane, Scopus, and Web of Science were searched for relevant articles using a search strategy including text words and subject headings related to TBI, IPV, and MHSU. Two reviewers independently assessed articles for inclusion. Results: The search identified 399 unique articles, 34 of which were included in this study. Of these, 11 articles reported on MHSU in IPV-related TBI and 9 articles reported on both TBI and MHSU in IPV but did not discuss the groups together. The remainder were reviews or theses that noted MHSU in IPV-related TBI. Included articles predominantly focused on cis-gendered women in heterosexual relationships and were conducted in the United States. Only three articles focused on the experiences of Black or Indigenous women and none of the included studies discussed implications of co-occurring TBI and MHSU on survivor’s healthcare-related needs or access to care. Conclusions: Despite the high rates of co-occurring TBI and MHSU among survivors of IPV, there is little research on this intersection and no investigation of the impacts on the health system. Future research should focus on identifying the healthcare-related needs of survivors and identifying and mitigating barriers to access.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".