Implementing an Intimate Partner Violence (IPV) Screening Protocol in HIV Care
Bibliographic record
Abstract
HIV and intimate partner violence (IPV) epidemics propagate and interact in a syndemic fashion contributing to excess burden of disease and poorer health outcomes. In order to understand the impact of IPV on HIV disease management, a universal screening program was implemented in the Southern Alberta Clinic in May 2009. We evaluated our IPV screening protocol and made recommendations for its usage in HIV care. IPV data obtained from patients were evaluated, supplemented with responses from a subset of in-depth interviews. 35% of 1721 patients reported experiencing IPV. Prevalence was higher among females (46%), Aboriginal Canadians (67%), bisexual male/females (48%), and gay males (35%). Of 158 patients interviewed, only 22% had previously been asked about IPV in any health care setting. Patients were responsive to routine IPV screening emphasizing that referral services need to be easily accessible. 23% of patients disclosing IPV subsequently connected to additional IPV resources after screening. We recommend that universal IPV screening be incorporated within regular HIV clinic care. The IPV survey should be given after trust has been established with regular follow-up every 6-12 months. A referral process to local agencies dealing with IPV must be in place for patients disclosing abuses.
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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 | 0.073 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".