Gaining GameSense: the antecedents and consequences of interacting with responsible gambling advisors
Bibliographic record
Abstract
Aims Given the risk and prevalence of excessive gambling, stakeholders have developed educational resources and tools to promote responsible gambling (RG) and minimize gambling-related harms. However, their effectiveness relies on players’ engagement with the programming. This research investigated factors that influence player engagement with RG ambassadors (i.e. GameSense Advisors; ‘Advisors’) at casinos in Massachusetts.Method Players (N = 303) who recently interacted with an Advisor completed an online survey about their experience.Findings Latent Class Analysis of the reasons for interacting with an Advisor revealed three distinct groups of players: ‘Comprehensive Information Seekers’, ‘RG Curious’, and ‘Externally Inspired’.Conclusions This research highlights the importance of tailoring engagement strategies to meet the diverse needs and interests of different players. By tailoring RG strategies to meet players’ various motivations, RG programs like GameSense can provide more effective RG education and support, enhancing player engagement and promoting healthier gambling practices across a broader range of players.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".