Gender differences in problem gambling behaviour from help-line callers
Bibliographic record
Abstract
The province of Manitoba, Canada, has operated a province-wide Problem Gambling Help-Line 24 hours a day, 7 days a week, since 1993. The present study looked at gender differences in a sample of help-line callers. A total of 97 callers (59 men and 38 women) were asked 34 questions. The results showed both similarities and differences among men and women. The most popular gambling activity for all callers was video lottery terminals (71%). Male and female callers had similar background demographics and had both experienced numerous financial, relationship, and work problems as a result of their gambling. Some gender differences were found. Female callers reported a shorter duration of their gambling problem compared to male callers. Higher numbers of men than women gambled in bars, hotels, and restaurants. Overall, the results contribute to an understanding of gender differences in problem gambling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".