A qualitative investigation of the feasibility and acceptability of lower risk gambling guidelines
Bibliographic record
Abstract
Effective and comprehensive harm reduction strategies to mitigate gambling-related harms are needed worldwide. The development of such strategies is however resource intensive. Using existing models in multiple contexts would thus be advisable. This study is part of a larger project investigating the feasibility and acceptability of the Canadian Lower Risk Gambling Guidelines (LRGG) within a Finnish cultural context. The Canadian guidelines recommend not gambling more than 1% of one's household income, not gambling more than 4 days per month, and to avoid regularly gambling at more than 2 types of gambling products.13 Focus group interviews were conducted (N = 37, 23 women, 14 men) across five subpopulations: individuals gambling at no-risk/low-risk levels, individuals with past experiences of problematic gambling, concerned significant others of those with gambling problems, professional gamblers, and social workers and health care professionals. The analysis utilised a deductive approach.While the subpopulations differed in their assessment of the LRGG in some regards, we were able to synthesise three concrete suggestions to adjust the Canadian LRGGs into the Finnish context. Participants proposed rephrasing the guidelines as follows: (1) Limit gambling to a fixed percentage of monthly personal income after taxes and other fixed expenses, (2) Restrict the number and duration of weekly gambling sessions, (3) Avoid regular participation in the most harmful forms of gambling, such as online casino games.Overall, the LRGG were considered as useful also in the Finnish context. However, our results suggest that some culturally specific rewording may be advisable. The main challenge in the implementation of the LRGG is that respondents across groups considered the guidelines to be aimed at someone else. Implementation therefore requires clear communication that these guidelines are for all individuals who gamble, not only those experiencing problems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".