Income Generation and Attitudes Toward Addiction Treatment Among People Who Use Illicit Drugs in a Canadian Setting
Bibliographic record
Abstract
Introduction: Socioeconomically marginalized people who use illicit drugs (PWUD) often engage in alternative income generating activities to meet their basic needs. These activities commonly carry a number of health and social risks, which may prompt some PWUD to consider addiction treatment to reduce their drug use or drug-related expenses. We sought to determine whether engaging in certain forms of income generation was independently associated with self-reported need for addiction treatment among a cohort of PWUD in Vancouver, Canada. Methods: Data from two prospective cohorts of PWUD in Vancouver were used in generalized estimating equations to identify factors associated with self-reported need for addiction treatment, with a focus on income generating activities. Results: Between June 2013 and May 2014, 1285 respondents participated in the study of whom 483 (34.1%) were female and 396 (30.8%) indicated that they needed addiction treatment. In final multivariate analyses, key factors significantly and positively associated with self-reported need for addiction treatment included engaging in illegal income generating activities (adjusted odds ratio [AOR] = 1.96, 95% Confidence Interval [CI}: 1.11-3.46); sex work (AOR = 1.61, 95% CI: 1.05-2.47), homelessness (AOR = 1.65, 95% CI: 1.22-2.25); and recent engagement in counselling (AOR = 1.85, 95% CI: 1.40-2.44). Discussion: Our results suggest that key markers of socioeconomic marginalization are strongly linked with a stated need for addiction treatment. These findings underscore the need to provide appropriate and accessible addiction treatment access to marginalized PWUD and to consider alternative approaches to reduce socioeconomic disadvantage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 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".