Public support of and attitudes toward decriminalization of possession of illegal drugs among the general population in British Columbia
Bibliographic record
Abstract
BACKGROUND: Decriminalization of possession of illegal drugs was implemented in British Columbia, Canada for a three-year period starting from January 31, 2023. As public sentiment played a large role in the outcome of a similar reform in Oregon, United States, it is important to examine the public’s perceptions of this policy. We examined public support of and attitudes toward decriminalization. METHODS: Data were from an online, non-probability, cross-sectional survey of adults who spoke English and resided in British Columbia (N = 1,202; March to April 2024). One item assessed support of decriminalization, and ten items assessed attitudes toward decriminalization. The analytical strategy involved descriptive statistics and multinomial logistic regression. RESULTS: In regards to support of decriminalization, 41% did not support decriminalization, 26% were neutral or not sure about supporting decriminalization and 33% supported decriminalization. The majority agreed that decriminalization will reduce criminalization of drugs (50%) and encourage drug use experimentation (53%). More agreed (44%) than disagreed (28%) that decriminalization made them feel less safe. The majority disagreed that decriminalization will reduce overdoses (55%) and drug-related crimes (50%). Older age was associated with an increased likelihood of not supporting decriminalization compared with being neutral or not sure about supporting decriminalization (OR [95% CI]: 1.97 [1.35 to 2.88] for 40 to 59 years old; 1.89 [1.19 to 3.02] for ≥ 60 years old), and female or another gender was associated with a decreased likelihood of supporting decriminalization compared with being neutral or not sure about supporting decriminalization (0.66 [0.47 to 0.91]). CONCLUSIONS: Public support of decriminalization was not strong, which may be rooted in lack of robust confidence in decriminalization to achieve its goals and increased public safety concerns. These findings necessitate ongoing evaluations of the policy and increasing knowledge mobilization activities to keep the public abreast of the latest developments.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".