Why do young adults gamble online? A qualitative study of motivations to transition from social casino games to online gambling
Bibliographic record
Abstract
The present research examined the mechanisms of initiating online gambling among young adults. Of particular interest was whether social casino gaming was noted as part of young adults’ experience with online gambling. This is because there is growing concern that social casino gaming may be a ‘gateway’ to online gambling. Three focus groups (N = 21) were conducted with young adult online gamblers from two large Canadian Universities. Participants noted the role of peer influence as well as incentives (e.g., sign up bonuses) as important factors that motivated them to start engaging in online gambling. Participants also noted a link between social casino games and online gambling. Specifically, several young adults reported migrating to online gambling within a relatively short period after engaging with social casino games. Potential mechanisms that may lead to the migration from social casino games to online gambling included the role of advertisements and the inflated pay out rates on these free to play gambling like games. The results suggest initiatives to prevent the development of disordered gambling should understand the potential of social casino gaming to act as a gateway to online gambling, especially amongst this vulnerable population.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".