“A whole ball of all-togetherness”: The interwoven experiences of intimate partner violence, brain injury, and mental health
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) is a global public health crisis, with physical violence leaving IPV survivors at high risk of brain injury (BI). Both BI and IPV have significant physical, psychological, cognitive, and social impacts, including a high risk of mental health concerns, yet there is limited exploration of IPV survivors' experiences with BI and mental health. This study aimed to explore the BI- and mental health-related needs and experiences of IPV survivors from the perspectives of survivors and service providers with the objective of developing knowledge translation materials to raise awareness and support survivors and service providers in addressing these concerns. METHODS: This qualitative interpretive description study involved 19 semi-structured interviews and two focus group discussions (2-3 participants each) with 24 participants including IPV survivors experiencing BI and mental health concerns as well as IPV, mental health, and BI service providers between October 2020 and February 2021. Three screening questions were used to identify probable BI among survivors. Participants across groups were an average of 48.5±12.7 years old and were predominantly cisgender women (96%), of European origin (75%), with a university degree (71%). Interviews were recorded, transcribed, and thematically analyzed. FINDINGS: Across interviews, participants spoke about IPV, BI, and mental health as being complex and interrelated experiences that have impacts across the survivor's life and extend well beyond the abusive relationship. Because of the underrecognized nature of BI in IPV, finding and accessing care requires persistence that survivors spoke of as being like "a full-time job." The benefit of making meaningful connections, particularly with other survivors, was highlighted. CONCLUSIONS: Recognition of BI as a contributing factor shaping the lived experience of IPV survivors; acknowledgement that the impacts of IPV, BI, and mental health are far reaching and long lasting; and reducing barriers to finding and accessing appropriate care are critical to better supporting IPV survivors with BI and mental health concerns. Clinicians should consider BI and its lingering impacts among the IPV survivors with whom they work. Health and social policy that supports integration of care and the reduction of unnecessary barriers should be a priority.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".