Analysis of the geographical accessibility of vape shops in the vicinity of Quebec’s secondary and college educational institutions
Bibliographic record
Abstract
INTRODUCTION: significant proportion of secondary school students and young adults in Quebec have experimented with electronic cigarettes (e-cigarettes). Both personal and environmental factors have been associated with the use of vaping products by youth. Geographical accessibility to the points of sale of these products may be one of these factors. The purpose of this study is to develop a profile of the spatial distribution of stores specializing in the sale of vaping products (vape shops) in the vicinity of secondary schools, colleges and CEGEPs in the province of Quebec. METHODS: We calculated the accessibility of businesses to account for geographical exposure. Analyses were conducted to provide a snapshot of the situation in Quebec and to identify associations between the characteristics of educational institutions and geographical accessibility to vape shops. RESULTS: A total of 299 vape shops were identified. Colleges are closer to a vape shop (median distance: 1.2 km) than are secondary schools (median distance: 2.3 km). Large private colleges located in urban areas are closer to specialized vape shops. Medium or large private secondary schools located in urban and more advantaged areas are also closer to a specialized vape shop. CONCLUSION: This study is a step in developing an understanding of the location of vaping product shops and their geographical accessibility to young people. Important to consider is the geographical accessibility of young people to non-specialized shops that also sell e-cigarettes and then any potential connections between geographical accessibility to such non-specialized shops and the use of vaping products by young people.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".