Protective factors associated with short‐term cessation of injection drug use among a Canadian cohort of people who inject drugs
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Strategies are needed to transition persons who inject drugs out of injecting. We undertook this study to identify protective factors associated with cessation of injection drug use. DESIGN AND METHODS: Data were derived from three prospective cohorts of people who use illicit drugs in Vancouver, Canada, between September 2005 and November 2011. Generalised estimating equations were used to examine protective factors and 6-month cessation of injection drug use. RESULTS: Our sample of 1663 people who inject drugs included 563 (33.9%) women, and median age was 40 years. Overall, 904 (54.4%) individuals had at least one 6-month injection cessation event. In multivariable analysis, protective factors associated with cessation of injection drug use included the following: having a regular place to stay [adjusted odds ratio (AOR) = 1.30; 95% confidence interval (CI) 1.13-1.48]; formal employment (AOR = 1.12; 95% CI 1.01-1.23); social support from personal contacts (AOR = 1.22; 95% CI 1.10-1.35); social support from professionals (AOR = 1.26; 95% CI 1.14-1.39); ability to access health and social services (AOR = 1.21; 95% CI 1.09-1.34); and positive self-rated health (AOR = 1.21, 95% CI 1.11-1.32). DISCUSSION AND CONCLUSIONS: Over half of people who inject drugs in this study reported achieving 6-month cessation of injection drug use, with cessation being associated with a range of modifiable protective factors. Policy makers and practitioners should promote increased access to stable housing, employment, social support and other services to promote cessation of injection drug use. [Luchenski S, Ti L, Hayashi K, Dong H, Wood E, Kerr T. Protective factors associated with short-term cessation of injection drug use among a Canadian cohort of people who inject drugs Drug Alcohol Rev 2016;35:620-627].
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".