Making Change: Attempts to Reduce or Stop Gambling in a General Population Sample of People Who Gamble
Bibliographic record
Abstract
Objective: This study examined past year attempts to reduce or quit gambling among people who gamble generally and those with gambling problems specifically. Methods: = 10,054) completed a survey of gambling, mental health and substance use comorbidity and attempts to reduce or quit gambling. The sample was weighted to match the gambling and demographic profile for the same subsample (i.e., past month gamblers) in a recent Canadian national survey. Results: 5.7% reported that they tried to cutback or stop gambling in the past year. As predicted, individuals making a change attempt had greater levels of problem gambling severity and were more likely to have a gambling problem. Of individuals with problem gambling, 59.8% made a change attempt. Of those, 90.2% indicated that they did this primarily on their own, and 7.7% accessed formal or informal treatment. Most people attempting self- change indicated that this was a personal preference (55%) but about a third reported feeling too ashamed to seek help. Over a third (31%) reported that their attempt was successful. Of the small group of people accessing treatment, 39% described it as helpful. Conclusions: Whereas gambling treatment-seeking rates are low, rates of self-change attempts are high. The public health challenge is to promote self-change efforts among people beginning to experience gambling problems, facilitate success at self-change by providing accessible support for use of successful strategies, and provide seamless bridges to a range of other treatments when desired or required.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".