Severe intimate partner violence is associated with all-cause mortality among women living with HIV
Bibliographic record
Abstract
OBJECTIVE: To examine the independent association between intimate partner violence (IPV) severity and all-cause mortality among women living with HIV (WLHIV). DESIGN: Cross-sectional questionnaire linked to longitudinal vital statistics data. METHODS: We examined the lifetime prevalence of IPV and age-standardized all-cause mortality rates by IPV severity reported by WLHIV. Lifetime IPV (emotional/verbal, physical, or sexual) severity was assessed as a categorical variable: no history of any IPV (none); experienced one or two forms of IPV (moderate); or experienced all three forms of IPV (severe IPV). Two separate logistic regression models examined associations between any IPV (vs. none) as well as IPV severity (none vs. moderate, severe) and all-cause mortality. RESULTS: At the time of interview (2007-2010), 260 participants self-identified as women with a median (Q1-Q3) age of 41 years (35-46). Of these women, the majority were unemployed (85%), 59% reported any IPV and 24% reported severe IPV. Of the 252 women followed until 31 December 2017, 25% (n = 63) died. Age-standardized all-cause mortality rates for WLHIV who experienced severe IPV were two-times higher than women with no history of IPV (44.7 per 1000 woman-years vs. 20.9 per 1000 woman-years). After adjustment for confounding, experiences of severe IPV (vs. none) were significantly associated with all-cause mortality (aOR = 2.42, 95% CI = 1.03-5.70). CONCLUSION: Although we found that any lifetime experience of IPV was not associated with all-cause mortality, women ever experiencing severe IPV were significantly more likely to die during the study period. This may suggest a need for increased trauma- and violence-aware 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".