“Using the right tools and addressing the right issue”: A qualitative exploration to support better care for intimate partner violence, brain injury, and mental health
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) is a global public health crisis. Often repetitive and occurring over prolonged periods of time, IPV puts survivors at high risk of brain injury (BI). Mental health concerns are highly prevalent both among individuals who have experienced IPV and those who have experienced BI, yet the interrelatedness and complexity of these three challenges when experienced together is poorly understood. This qualitative study explored care provision for IPV survivors with BI (IPV-BI) and mental health concerns from the perspectives of both survivors and providers. METHODS: This qualitative interpretive description study was part of a broader research project exploring employment, mental health, and COVID-19 implications for survivors of IPV-BI. Participants (N = 24), including survivors and service providers, participated in semi-structured group and individual interviews between October 2020 and February 2021. Interviews were recorded, transcribed, and thematically analyzed. FINDINGS: Four themes were developed from interview findings: 1) identifying BI and mental health as contributing components to survivors' experiences is critical to getting appropriate care; 2) supporting survivors involves a "toolbox full of strategies" and a flexible approach; 3) connecting and collaborating across sectors is key; and 4) underfunding and systemic barriers hinder access to care. Finally, we share recommendations from participants to better support IPV survivors. CONCLUSIONS: Identifying both BI and mental health concerns among IPV survivors is critical to providing appropriate supports. Survivors of IPV experiencing BI and mental health concerns benefit from a flexible and collaborative approach to care; health and social care systems should be set up to support these 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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".