Spatial distribution of gambling: two indexes in support of the reduction of health inequalities
Bibliographic record
Abstract
Abstract Background Many studies have showed that disadvantaged areas residents have greater access to gambling sites and are more affected by gambling. Our research proposes an innovative method to characterize gambling environments in Quebec and addresses social inequality with respect to gambling exposure. Methods This cross-sectional ecological study was carried out in 3 stages: a Gambling Exposure Index (GEI) was built and is composed of 3 dimensions: spatial accessibility to gambling sites, density of gambling places, and relative risk associated with the types of game. The two-step floating catchment area (2SFCA) method was used to combine these dimensions into an overall GEI index. Data was retrieved from a geocoded directory of gambling sites and commercial databases. The relative risk of games is expressed by prevalence rates for those specific games in a Quebec population prevalence survey. A Vulnerability to Gambling Index (VGI) was produced based on 6 socio-economic proxies of problem gambling from the 2016 Canadian census, which were weighted and aggregated at the dissemination area (DA) level. Spatial and descriptive statistical analyses were conducted to explore the relationship between VGI and GEI, and to identify highly exposed and vulnerable areas. Results Our analyzes reveal significant associations between the GEI and the VGI in 2 599 out of 13 420 Quebec DAs (p < 0.05). Sectors with a high GEI show an average distance to the closest gambling sites of 2.8 km compared with 13.5 km for more advantaged sectors. Conclusions The interactive online mapping of the two indexes and statistical analysis of the results are beneficial to the professionals working in several fields such as risk monitoring, management of zoning, licensing and gambling distribution, prevention and treatment services. The method and the associated tools can be adapted to address the problem of increased accessibility to other unhealthy products in vulnerable neighborhoods. Key messages Two innovative ecological indexes show that increased accessibility to gambling correlates with a higher vulnerability to gambling in many Quebec regions. The online interactive map on gambling exposure and vulnerability provides reliable criteria to municipal, regional and governmental bodies for a safer distribution of gambling offer.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".