The Intersection of Intimate Partner Violence, Traumatic Brain Injury, and Mental Health in a Canadian Healthcare Context
Bibliographic record
Abstract
Intimate partner violence (IPV) is a highly prevalent public health concern, impacting 44% of Canadian women in their lifetime. Physical violence experienced in IPV puts survivors at high risk of brain injury (BI); however, BIs are often overlooked in IPV survivors. Both BI and IPV have significant physical, psychological, and social impacts, including a high risk of mental health (MH) concerns, yet the interrelatedness and complexity of these challenges is poorly understood, limiting health research, policy, and practice. This dissertation explored the implications of co-occurring BI and MH concerns for IPV survivors, particularly related to healthcare. A scoping review, guided by Arksey and O’Malley’s framework, explored the identification of and relationships between BI, MH, and IPV in the literature and the implications for health policy and practice. A qualitative interpretive description study, involving semi-structured interviews with 24 participants including survivors and service providers, explored 1) the BI- and MH-related needs and experiences of IPV survivors and 2) barriers and facilitators to providing/receiving appropriate care for IPV survivors with BI and MH concerns. The scoping review included 28 articles with highly variable methods for identifying IPV, BI, and MH across studies. In general, MH was more prevalent/MH scores were higher among IPV survivors with BI than without. The qualitative study identified IPV, BI, and MH as being complex and interrelated experiences with impacts extending beyond the abusive relationship. Identifying BI and MH as contributing to survivors’ experiences was deemed critical to getting appropriate care, noting that, due to the underrecognized nature of BI in IPV, finding and accessing care requires persistence that survivors spoke of as being like “a full-time job.” To appropriately support survivors, service providers need a “toolbox full of strategies” and a flexible approach, including connecting and collaborating across sectors. Challenges with accessing and providing care and recommendations from participants to better support IPV survivors’ care were also discussed. BI and MH are highly prevalent among IPV survivors and identifying both concerns is critical to charting a path forward for support and treatment. Health and social care systems should be organized to support flexible and collaborative approaches.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".