Factors associated with employment among a cohort of injection drug users
Bibliographic record
Abstract
INTRODUCTION AND AIMS: One of the most substantial costs of drug use is lost productivity and social functioning, including holding of a regular job. However, little is known about employment patterns of injection drug users (IDU). We sought to identify factors that were associated with legal employment among IDU. DESIGN AND METHODS: We describe the employment patterns of participants of a longitudinal cohort study of IDU in Vancouver, Canada. We then use generalised estimating equations (GEE) to determine statistical associations between legal employment and various intrinsic, acquired, behavioural and circumstantial factors. RESULTS: From 1 June 1999 to 30 November 2003, 330 (27.7%) of 1190 participants reported having a job at some point during follow up. Employment rates remain somewhat stable throughout the study period (9-12.4%). Factors positively and significantly associated with legal employment in multivariate analysis were male gender (adjusted odds ratio [AOR] = 2.78) and living outside the Downtown Eastside (AOR = 1.85). Factors negatively and significantly associated with legal employment included older age (AOR = 0.97); Aboriginal ethnicity (AOR = 0.72); HIV-positive serostatus (AOR = 0.32); HCV-positive serostatus (AOR = 0.46); daily heroin injection (AOR = 0.73); daily crack use (AOR = 0.77); public injecting (AOR = 0.50); sex trade involvement (AOR = 0.49); recent incarceration (AOR = 0.56); and unstable housing (AOR = 0.57). DISCUSSION AND CONCLUSIONS: Our results suggest a stabilising effect of employment for IDU and socio-demographic, drug use and risk-related barriers to employment. There is a strong case to address these barriers and to develop innovative employment programming for high-risk drug users.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".