Impacts of Canadian drinking age laws on sexual assault victimization of young women, 2009-2013
Bibliographic record
Abstract
Background Minimum legal drinking age (MLDA) laws are a widely used and effective strategy for reducing alcohol-related harms among youth. In Canada, the MLDA is 18 years in Alberta, Manitoba, and Québec (MLDA-18), and 19 years in the rest of the country (MLDA-19). Recently, experts have called for a national increase in the MLDA to 21 years. Few studies have examined the potential impacts of drinking-age laws on patterns of violent sexual victimization. The current study addresses this gap. Methods Regression discontinuity analyses utilizing data on police-reported sexual assault victimization incidents from Canada’s national Uniform Crime Reporting Survey (UCR), 2009-2013. Results For women just older than the MLDA, sexual assault victimization at any location increased in MLDA-19 provinces [22.5% (1.2%-43.4%) P = 0.039], compared to women just younger. Sexual victimization of women at bars and restaurants also increased in MLDA-18 provinces by 107.5% (95% CI 16.2%-201.1%; P = 0.021), and nationally by 69.2% (95% CI 1.1%-139.2%; P = 0.046), as well as at open air settings in MLDA-19 provinces by 35.4% (95% CI 5.9%-64.9%; P = 0.019) and nationally by 23.2% (95% CI 3.7%-42.7%; P = 0.020). Sexual assault victimizations by strangers also increased at bars/restaurants in MLDA-18 provinces [356.9% (95% CI 89.8%-631.2%; P = 0.009)] and nationally [189.0% (95% CI 63.3%-320.0%; P = 0.003)]; and in open air settings in MLDA-19 provinces [41.4% (95% CI 12.0%-71.3%; P = 0.006)]. Conclusions Immediately after the drinking age, young women experienced significant and immediate increases in sexual assault victimization. Increases were observed specifically at public settings associated with drinking venues. The results are supportive of the potential for MLDA laws to reduce harms related to sexual violence experienced by young women, and also highlight the importance of drinking context for informing interventions to reduce alcohol-related sexual assault. Key messages: This study provides the first evidence of the impact of drinking age laws on police reported sexual assault victimization among young women. Exit from Canadian legal drinking age laws was associatedwith increases in police-reported sexual assault victimization of young women, especially at public drinking settings
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".