Problematic gambling among the LGBTQIA2S + population in Canada: A quantitative study
Bibliographic record
Abstract
BACKGROUND AND AIMS: LGBTQIA2S + populations are believed to be at higher risk of problem gambling due to their elevated rates of mental disorders and substance abuse compared to heterosexual and cisgender populations. However, little is known about these populations regarding their gambling practices in the Canadian context. METHODS: We conducted an online survey among Canadian residents 18 years or older who self-identify as sexually and gender-diverse (i.e., LGBTQIA2S + ) and have gambled at least once in the preceding year (N = 1,519). We used descriptive analysis to portray the sample's gambling habits and a logistic regression model to identify potential factors associated with moderate-to-high-risk gambling. RESULTS: The prevalence of problematic gambling among our sample was 19.6%. This proportion did not vary according to sex or gender identity. Simultaneously, there was a negative relationship between age group and problematic gambling, and a positive relationship existed with gambling involvement. Logistic regression showed factors associated with higher odds of problematic gambling, including gambling frequency, gambling on slot machines, video lottery machines or poker, presenting other behavioral addictions, and poor mental health. Increasing age, identifying with White ethnicity, higher household income, and identifying as pansexual or queer were inversely correlated factors. DISCUSSION AND CONCLUSIONS: Sociodemographic factors associated with problematic gambling likely have complex underlying relationships that merit further research. Gambling formats with faster reward responses presented the highest prevalence of problematic gambling. Further analysis by identity subgroups, and research on their experiences with gambling harm, health and social services, and discrimination could provide insight into the needs and challenges of this population.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".