“I Don’t Have Any Limits”: A Qualitative Analysis of Individual Gambling Self-Control Strategies
Bibliographic record
Abstract
Despite existing knowledge on self-control strategies in the context of problem gambling, further insight is needed to understand a broader spectrum of self-control strategies among individuals who span the continuum of problem gambling. This qualitative study drew on the experiences and perceptions of individuals engaging in recreational gambling as well as those at the at-risk and problem gambling levels to explore various self-control strategies and their nuances. Thirty semi-structured interviews, guided by open-ended questions exploring how gamblers define and practice responsible gambling and their understanding of responsible gambling interventions, were conducted in Quebec, Canada. Thematic analysis identified three main themes: setting limits on frequency, time, and spending, playing smart (i.e., mindful gambling), and recognizing strategy limitations. Despite employing various strategies, participants struggled to maintain self-control. Maintaining self-control was particularly difficult for those with higher Problem Gambling Severity Index scores. These findings underscore the complexities of managing gambling behavior and, more specifically, these findings contribute to understanding the role of self-control in mitigating gambling problems. This study highlights the need to focus on developing comprehensive support systems and harm minimization measures within gambling environments to better support individuals across the gambling spectrum.
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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".