Experiences of violence and hospitalization rates among people living with HIV in British Columbia, Canada
Bibliographic record
Abstract
People living with HIV (PLWH) in Canada experience high rates of interpersonal violence which may lead to adverse health outcomes that require hospitalization. Using self-reported data on experiences of violence linked to administrative health data on hospitalizations, we used Poisson regression modelling to examine and compare the associations between experiences of violence (recent [in the past 6 months], non-recent [>6 months ago], or none) and hospitalization rates, among a sample of PLWH in British Columbia, Canada. Of 984 PLWH included in this study, 60.0% reported experiencing non-recent violence, and 14.8% experienced recent violence. Those who experienced non-recent violence had a higher rate of hospitalization than those who never experienced violence (adjusted Rate Ratio [aRR]: 1.41; 95% Confidence Interval [CI]: 1.05-1.87). There was no difference in hospitalization rates between those who experienced recent violence and those who never did (aRR: 1.08; 95% CI: 0.74-1.60). PLWH who experienced recent violence had the highest proportion of hospitalizations attributed to mental, behavioural, or neurodevelopmental disorders. Efforts are needed to provide violence-aware care that recognizes violence and its impacts on PLWH experiencing multiple sociostructural inequities. Further studies should evaluate the impacts of violence on other types of healthcare utilization in generalizable samples of PLWH in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".