The impact of social, structural and physical environmental factors on transitions into employment among people who inject drugs
Bibliographic record
Abstract
Despite growing awareness of the importance of context for the health of people who use drugs, studies examining labour market outcomes have rarely considered the role that physical, social and structural factors play in shaping labour market participation among drug users. Using discrete time event history analyses, we assessed associations between high-intensity substance use, individual drug use-related risk and features of inner-city drug use scenes with transitions into regular employment. Data were derived from a community-recruited cohort of people who inject drugs in Vancouver, Canada (n=1579) spanning the period of May 1996 to May 2005. Results demonstrate that systematic socio-demographic differences in labour market outcomes in this context generally correspond to dimensions of demographic disadvantage. Additionally, in initial analyses, high-intensity substance use is negatively associated with transitions into employment. However, this negative association loses significance when indicators measuring exposure to physical, social and structural features of the broader risk environment are considered. These findings indicate that interventions designed to improve employment outcomes among drug users should address these social, structural and physical components of the risk environment as well as promote the cessation of drug use.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".