Employment predicts decreased mortality among HIV-seropositive illicit drug users in a setting of universal HIV care
Bibliographic record
Abstract
Objective: Given the link between employment and mortality in the general population, we sought to assess this relationship among HIV-positive people who use illicit drugs (PWUD) in Vancouver, Canada. Methods: Data were derived from a prospective cohort study of HIV seropositive PWUD (n=666) during the period of May 1996-June 2010 linked to comprehensive clinical data in Vancouver, Canada, a setting where HIV care is delivered without charge. We estimated the relationship between employment and mortality using proportional hazards survival analysis, adjusting for relevant behavioural, clinical, social and structural factors. Results: In a multivariate survival model, a time-updated measure of full time, temporary or self-employment compared to no employment was significantly associated with a lower risk of death (adjusted hazard ratio = 0.44, 95% CI: 0.22-0.91). Results were robust to adjustment for relevant confounders, including age, injection and non-injection drug use, baseline CD4+ T-cell count and baseline plasma viral load. Conclusions: These findings suggest that employment may be an important dimension of mortality risk of HIV-seropositive illicit drug users. The potentially health-promoting impacts of labour market involvement warrant further exploration given the widespread barriers to employment among this highly marginalized population and persistently elevated levels of preventable mortality.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".