Naloxone awareness and acquisition: Findings from the 2021‒2022 Canadian Postsecondary Education Alcohol and Drug Use Survey
Bibliographic record
Abstract
OBJECTIVES: This cross-sectional study assessed naloxone awareness, acquisition rates, and reasons for acquisition among postsecondary students in Canada aged 17‒25 years. METHODS: Using data from the 2021‒2022 Canadian Postsecondary Education Alcohol and Drug Use Survey, we conducted descriptive analyses of 31,643 students to characterize naloxone awareness, acquisition, and reasons for acquisition overall and by age, gender, race, international student status, and opioid pain reliever (OPR) use. Using multivariable logistic regression, we assessed the relationship between demographic variables and naloxone awareness and acquisition. RESULTS: Among postsecondary students in Canada, only 47% had heard of naloxone, and only 5% had acquired it in the past year. Significant predictors of naloxone awareness and acquisition included gender, age, race, international student status, and OPR use. Older students, non-binary students, domestic students, and Indigenous students had higher odds of both naloxone awareness and acquisition. Students who had used OPRs in the past year were less likely to be aware of naloxone (AOR = 0.85, 95% CI: 0.80-0.91). However, among those who were aware, they were more likely to have acquired naloxone (AOR = 1.16, 95% CI: 1.01-1.34) than those who had not used OPRs. Among students who had acquired naloxone in the past year, 97% reported their main reason for obtaining it was for use in emergencies involving other people. CONCLUSION: Low naloxone awareness and acquisition among postsecondary students in Canada represent an important public health gap. Increasing naloxone awareness and acquisition may play an important role in enhancing safety on campuses and beyond.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".