Testing the acceptability and feasibility of the lower-risk gambling guidelines in Finland
Bibliographic record
Abstract
Background: The lower risk gambling guidelines (LRGG) represent an evidence-based collaborative effort to provide clear advice to people on the limits of safe gambling consumption. The guidelines are as follows: 1) Gamble no more than 1% of household income per month; and 2) Gamble no more than 4 days per month; and 3) Avoid regularly gambling at more than 2 types of games. Methods: In an online survey study (N = 778), we evaluated the feasibility and acceptability of the LRGG among different subpopulations in Finland. Results: We found that the guidelines were generally evaluated positively as understandable, sensible, clear, and "just right" in terms of their content. There were some notable differences between subpopulations: Individuals who were at risk of gambling problems evaluated the LRGG more negatively than others, while professionals working in the field of gambling prevention were the most optimistic about the guidelines. Thus, increased level of potentially harmful gambling engagement was linked with a somewhat more pessimistic attitude towards the guidelines. On the other hand, those who had not gambled in the past year viewed the guidelines as too permissive compared with those who had gambled, or those working in gambling prevention. Discussion: Overall, our results show clear differences of opinion between the various subpopulations, which appear to be associated with the individuals' level and nature of gambling experience. We conclude that the LRGG can likely be adopted into wider use in Finland.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".