Players’ perceptions of, and engagement with, the GameSense responsible gambling program in Massachusetts casinos
Bibliographic record
Abstract
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, the effectiveness of such programs relies on players’ awareness of, and their engagement with, such programming. This research investigated factors that influence player engagement with the GameSense RG program. We surveyed 1109 regular players across three casinos in Massachusetts where GameSense is used. We found that most players (73.1%) were aware of GameSense and that 17.6% had engaged with the program. In terms of reasons for visiting a Center, Latent Class Analysis revealed two distinct classes: ’Information Seekers’ and ”Curiosity and Swag Inspired’. As for those who had yet to visit a Center, three classes emerged: ‘Invitation Responsive’, ‘Self-Assured Non-Believers’, and ‘Self-Assured’. Although GameSense is for the broad spectrum of gamblers, we found that players who engaged in more RG behaviors were less likely to engage with the program. The results suggest encouraging all players to engage with GameSense may be a challenge. Efforts are needed to increase awareness and engagement of the program across the broad spectrum of gamblers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".