The shifting riskscape: Changes in geographic accessibility to alcohol, cannabis, and tobacco in Ontario, Canada from 2019 to 2022
Bibliographic record
Abstract
BACKGROUND: Geographic accessibility to alcohol, cannabis, and tobacco has changed in Ontario, Canada between 2019 and 2022 due to regulatory changes. AIM: We conceptualize geographic accessibility to these substances as a 'riskscape' and investigate how these recent regulatory changes have altered the substance riskscape in Ontario, Canada in relation to neighborhood socioeconomic factors. DESIGN: We use an interrupted time series design to calculate the shortest distance between the centroid of each dissemination block and the nearest alcohol, cannabis, and tobacco outlet in 2019 and 2022. The geographic accessibility to substance outlets is further evaluated in relation to neighborhood disadvantage. FINDINGS: Accessibility to alcohol and cannabis increased substantially across the province, while there were minimal reductions in accessibility to tobacco during the same time period. Neighborhoods with a higher prevalence of lone parenthood, low educational attainment, and government transfer recipients exhibited a higher exposure to risky substances compared to the general population. The regulatory changes further exacerbated potential vulnerability among these populations to varying degrees. CONCLUSIONS: The substantial changes in accessibility to alcohol, cannabis, and tobacco warrant further investigation, particularly focusing on how these shifting riskscapes influence consumption patterns, disparities in harm, and long-term health outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".