Video lottery terminal access and gambling among high school students in Montréal.
Bibliographic record
Abstract
BACKGROUND: Gambling is a risky behaviour that involves uncertain financial outcomes, can be addictive, and has been associated with strongly adverse social and public health outcomes. We wanted to assess whether socio-economic and gambling-related-opportunity environments of neighbourhoods affected the uptake of video lottery terminal (VLT) gambling among Montréal youth. METHODS: Spatial and statistical analyses were conducted to examine geographical patterns of neighbourhood socio-economic conditions, VLT sites (n=407), and high school locations (n=305) within the Montréal Census Metropolitan Area (CMA). VLT concentration within high school neighbourhoods was measured to examine how the number of VLT opportunities varies according to socio-economic status of the school neighbourhood. A student survey was analyzed using logistic regression analysis to explore the role of individual (student) characteristics and environmental (neighbourhood) characteristics in predicting the VLT gambling behaviours reported among a sample (n=1206) of high school students. RESULTS: Video lottery gambling opportunities are more prevalent near schools located in socio-economically deprived neighbourhoods compared with schools located in more affluent neighbourhoods. The principal individual risk factors for VLT gambling were shown to be male sex, peer VLT-use, substance use, as well as the after-school routines of youth. INTERPRETATION: The spatial distribution of VLTs reflects local geographies of socio-economic disadvantage and may have a pronounced impact on students attending schools in lower income neighbourhoods, especially those with individual risk factors. Efforts to reduce gambling-related public health costs may want to take into account the socio-spatial distribution of gambling opportunities, particularly in the local environments that youth frequent.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".