Beyond description: Understanding gender differences in problem gambling
Bibliographic record
Abstract
Background and aims Though women make up roughly one third of all problem gamblers, research has typically focused on male problem gamblers. Recent research has started to shift its attention toward the importance of gender. However, studies rarely attempt to understand gender differences in problem gambling or subject these differences to thorough multivariate analyses. To address some of the gaps in our knowledge of gender differences, we examine whether patterns of gambling behavior and psychological factors mediate the relationship between gender and problem gambling. Methods We use logistic multiple regression to analyze two large Canadian datasets - the 2005 Ontario Prevalence Survey and the 2007 Canadian Community Health Survey. Results Variables found to mediate the relationship between gender and problem gambling are the type(s) of game(s) played (in the 2005 Ontario Prevalence Survey) and the number of games played (in the 2007 Canadian Community Health Survey). Conclusions Men are more likely to be problem gamblers than women, and this gender difference is understandable in terms of differences in patterns of gambling behavior. We conclude that men experience problems because they play riskier games and women experience problems because they prefer chance-based games, which are associated with significantly higher odds of problem gambling. We specify the three main ways that women's reasons for gambling - to escape or for empowerment - translate into chance-based games.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".