Knowledge of Fentanyl and Perceived Risk of Overdose Among Persons Who Use Drugs in Vancouver, Canada
Why this work is in the frame
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Bibliographic record
Abstract
Objectives: In North America, the illicit drug supply is increasingly contaminated by illicitly manufactured fentanyl. We sought to assess the level and source of fentanyl risk knowledge, defined as knowledge of the overdose risks associated with fentanyl, and characterize the prevalence and correlates of perceived risk of personally having a fentanyl overdose among persons who use illicit drugs (PWUD) in Vancouver, British Columbia. Methods: We derived data from 3 prospective cohorts of PWUD in Vancouver from December 2016 through May 2017. We used multivariable ordinal regression analysis to identify factors associated with a lower perceived risk of having a fentanyl overdose. Results: Of 1166 participants, 1095 (93.9%) had fentanyl risk knowledge. Of 1137 participants who answered questions about their perceived risk of having a fentanyl overdose, 398 (35.0%) perceived having no risk, 426 (37.5%) perceived having low risk, and 313 (27.5%) perceived having moderate or high risk. Never or rarely using opioids (n = 541, 65.7%) was the most common reason for reporting no or low perceived risk (n = 824), whereas 137 (16.6%) participants reported daily heroin use. In multivariable analysis, compared with participants who perceived a moderate or high risk, participants who perceived a lower risk were less likely to report a recent nonfatal overdose (adjusted odds ratio [aOR] = 0.28; P < .001), recent injection drug use (aOR = 0.34; P < .001), and awareness of recent exposure to fentanyl (aOR = 0.34; P < .001). Conclusion: Despite a high level of fentanyl risk knowledge, most study participants did not translate this knowledge into a risk of having an overdose. Although participants who perceived a lower risk were less likely to have had an overdose, a considerable proportion was engaged in daily opioid use, suggesting the need to improve overdose prevention efforts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it