From the mouths of social media users: A focus group study exploring the social casino gaming–online gambling link
Bibliographic record
Abstract
Background and aims The potential link between social casino gaming and online gambling has raised considerable concerns among clinicians, researchers and policy makers. Unfortunately, however, there is a paucity of research examining this potential link, especially among young adults. This represents a significant gap given young adults are frequently exposed to and are players of social casino games. Methods To better understand the potential link between social casino games and online gambling, we conducted three focus groups (N = 30) at two large Canadian Universities with college students who were avid social media users (who are regularly exposed to social casino games). Results Many participants spontaneously mentioned that social casino games were a great opportunity to build gambling skills before playing for real money. Importantly, some participants expressed a belief that there is a direct progression from social casino gaming to online gambling. Conversely, others believed the transition to online gambling depended on a person's personality, rather than mere exposure to social casino games. While many young adults in our focus groups felt immune to the effects of social casino games, there was a general consensus that social casino games may facilitate the transition to online gambling among younger teenagers (i.e., 12-14 yr olds), due to the ease of accessibility and early exposure. Discussion The results of the present research point to the need for more study on the effects of social casino gambling as well as a discussion concerning regulation of social casino games in order to minimize their potential risks.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".