The last 10 years: any changes in perceptions of the seriousness of alcohol, cannabis, and substance use in Canada?
Bibliographic record
Abstract
BACKGROUND: Over the last decade, there have been a number of changes in the Canadian landscape - the deconstruction of alcohol policy in some provinces, the legalization of cannabis, increased availability of gambling options, and the increase in opioid use and its associated problems. Have there been concomitant changes in societal images of addictions? METHODS: A general population survey on societal images of addictions was conducted in multiple countries in 2008 - Finland, Sweden, Canada (Canadian sample size: N = 864; 40% response rate), and part of Russia (St Petersburg). We repeated the same survey in 2018 in Canada (N = 813; response rate = 23%). The survey assessed perceptions of the seriousness of different issues to society - including items about alcohol, tobacco, marijuana, gambling, misuse of medical drugs, and drugs like amphetamine, cocaine, or heroin - among other items (e.g., pollution, violent crime, prostitution). RESULTS: There were increases in perceptions of the seriousness of misuse of medical drugs (p = .001), of illicit drugs (p = .005), ratings of the seriousness of cannabis use (p = .02), and a decrease in ratings of gambling as a social problem (p = .04). Ratings of the seriousness of alcohol and tobacco as social problems did not display significant changes over time (p > .05). CONCLUSIONS: There has been some variation in societal perceptions of the seriousness of different addictions. Increases in perceptions of the seriousness of misusing medical drugs and the use of illicit drugs may reflect increases in societal concerns about opioid use and its associated problems. Despite substantial changes in alcohol control policies, the legalization of cannabis, and the increased availability of options for gambling, there appears to be very little associated change in societal perceptions regarding these addictive behaviours.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".