Domestic Violence Screening: Prevalence and Outcomes in a Canadian HIV Population
Bibliographic record
Abstract
There is a strong association between domestic violence victimization and HIV infection. This may lead to poor health outcomes including mental health disorders and reduced access to care. A standardized domestic violence screening interview was incorporated into ongoing care in the large and diverse population living with HIV in Southern Alberta, Canada. Results from May through December 2009 are reported, including the prevalence and outcomes of abuse. Thirty-four percent of 853 patients screened reported abuse. Of these, 16% reported abuse in their current relationship, 58% in a previous relationship, and 57% reported a history of childhood abuse. High-risk groups for abuse included females (43%), gay/bisexual males (35%), and Aboriginals (61%). We found an association between a history of domestic violence and delayed access to care (p < 0.05), missed appointments (p < 0.001), and an increased use of clinic resources such as social work (p < 0.0001) and psychiatry (p < 0.001). Mental health conditions prior to HIV diagnosis, including depression (p < 0.0001), suicidal ideation (p < 0.0001), and anxiety disorder (p < 0.0001) were associated with abuse at any time, while a history of adjustment disorder was associated with childhood abuse (p < 0.05). A simple domestic violence screening tool was helpful for identifying patients experiencing abuse in our diverse HIV-infected population. This high prevalence of domestic violence among our HIV patients was associated with poor outcomes and an increased use of medical resources. HIV caregivers should be aware of domestic violence in order to optimize care and refer patients to appropriate support professionals as needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".