Exploring the intersection of brain injury and mental health in survivors of intimate partner violence: A scoping review
Bibliographic record
Abstract
Rationale: Intimate partner violence (IPV) is the most commonly occurring form of violence against women. The most common site of injury in IPV is the head, face, and neck, resulting in possible brain injury (BI). Independently, mental health (MH) concerns are highly prevalent among both IPV survivors and individuals with BI; however, no systematic review exists on the combined experience of BI and MH in IPV. Objective: The aim of this review was to describe the identification of and relationships between BI, MH, and IPV in the literature and the implications for health policy and practice. Methods: A search strategy including text words and subject headings related to BI, IPV, and MH was developed for MEDLINE and translated to EMBASE, PsycINFO, CINAHL, Cochrane, Scopus, and Web of Science. Two reviewers independently assessed articles for inclusion. Articles discussing MH, BI, and IPV in relation to one another were included in the review. Results: Twenty-eight articles were identified for inclusion. Methods for identifying IPV, BI, and MH were highly variable across studies. Fourteen studies reported significantly higher MH scores in IPV survivors with BI than in those without BI. Articles predominantly focused on cis gender women in heterosexual relationships and the impact of race and ethnicity were largely overlooked. Healthcare access was explored by eight articles, though none discussed the implications of co-occurring BI and MH. Conclusion: Brain injury and MH are highly prevalent among IPV survivors; however, little research discusses the implication for healthcare. Future research should explore healthcare-related needs and experiences to inform policy and practice and better represent the diversity of IPV survivors.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".