Changes in drug use behaviors coinciding with the emergence of illicit fentanyl among people who use drugs in Vancouver, Canada
Bibliographic record
Abstract
Background: With the emergence of illicitly-manufactured fentanyl, drug overdose deaths have risen in unprecedented numbers. In this context, there is an urgent need to characterize potential changes in drug use behaviors among people who use drugs (PWUD).Objective: To examine changes in drug use behaviors following the emergence of illicit fentanyl among people who use drugs (PWUD).Methods: Data for this cross-sectional analysis was derived from three prospective cohorts of PWUD between December 2016 and May 2017 in Vancouver, Canada. Multivariable logistic regression was used to determine factors associated with self-reported behavior changes (binary variable “yes” or “no”) following the emergence of illicit fentanyl.Results: Among 999 participants [363 (36.3%) females], 388 (38.8%) reported some behavior change. The remaining 611 (61.2%) reported no change in behavior; 240 (39.3%) of these individuals had recently been exposed to fentanyl. In multivariable analyses, factors independently associated with behavior change included recent non-fatal overdose (Adjusted Odds Ratio [AOR] = 2.28), active injection drug use (AOR = 1.96), being on opioid agonist therapy (AOR = 1.80), and urine drug screen positive for fentanyl (AOR = 1.45), (all p < .05).Conclusion: The majority of PWUD in our sample did not change their drug use behavior despite a high prevalence of fentanyl exposure, indicating a need for targeted behavior change messaging and overdose prevention efforts such as naloxone and addiction treatment for this sub-population of PWUD. Further, the high fentanyl exposure observed in our sample suggests a need to address upstream structural factors shaping the overdose risk in addition to individual behavioral change.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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".