Nudge theory and gambling: a scoping review
Bibliographic record
Abstract
Background: Gambling disorder (GD) is a pressing public health concern with significant societal costs. The recently developed nudge theory, which is rooted in behavioral economics, aims to influence the decision-making behaviors of individuals by implementing changes in the environment. Aim: This scoping review aims to synthesize the literature on nudge theory as it relates to gambling. Methods: This scoping review accords with the Arksey and O'Malley framework, as refined by Levac et al. It includes only articles from peer-reviewed journals that focus, as main themes, on both nudge theory and gambling. The final study selection includes six articles. Results: The scoping review process led to studies explaining how (1) nudges aim to prod people toward healthier gambling choices, fostering the adoption of more responsible gambling practices, and (2) some gambling features, called dark nudges (or sludges), exploit and harm the decision-making processes of people who gamble. Conclusion: This scoping review highlights the fact that many stakeholders are involved in the field of gambling, and that better cooperation between them would promote safer and more responsible gambling practices. Future research is also needed to empirically test nudges to develop a better understanding of their impact on those who gamble.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".