The Profiles of People who Do not Engage in Positive Play while Gambling
Bibliographic record
Abstract
BACKGROUND: Positive Play describes non-problematic gambling behaviour. The term avoids the ambiguity of the term 'Responsible Gambling', and the negative connotations associated with 'Problem Gambling'. This purpose of the paper was to identify demographic groups who score consistently low on the positive play in order to determine where interventions are most needed. METHODS: The study is a secondary analysis of general population data, collected online by AskingCanadians on behalf of Ontario Lottery and Gaming that examined data on positive play and harm reduction related to gambling. The paper included the analysis of survey data from players who reported engaging in one of the four target gambling activities (N = 3701): Lotteries (n = 1832), casinos (n = 1272), online (n = 300), and charity bingo (n = 297). RESULTS: People who scored low on all four of the Positive Play scales were more likely to be young, male, single, have a low or middle range income, have at least some university education, and to have been born outside of Canada. In addition, people who scored low on Positive Play scale were more likely to play online games or charity games compared to lotteries. These findings were mostly consistent across game types however due to the small sample sizes for online gamblers and charitable gamblers, some effects did not reach significance. DISCUSSION: Awareness efforts for Positive Play should be directed at younger players, males, and recent immigrants. In addition, more effort in encouraging Positive Play needs to be directed at online gamblers and bingo gamblers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".