Untreated young gamblers with game-specific problems: Self-concept involving luck, gambling ecology and delay in seeking professional treatment
Bibliographic record
Abstract
We assessed whether self-perceptions of dispositional personal luck are more pronounced among young gamblers who experience trouble with types of games that contain elements of skill relative to gamblers who have trouble with games characterized by pure chance. We also examined the convergence of subjective belief in personal luck and ecology of the game on attitudes toward seeking treatment. Our methodology consisted of a survey involving a sample of untreated young adults who scored one or more on the DSM-IV gambling screen. Results showed young people whose most problematic game contained a skill component (e.g., poker) were more likely to perceive themselves as being characterologically lucky. They also had more negative attitudes toward treatment seeking than their youthful counterparts whose most problematic game was one of pure chance (e.g., slot machines). These results suggest the subjective belief that one is a lucky person may play a role in maintaining problematic patterns of gambling. At the same time, this type of an unrealistic self-concept may also undermine the motivation to reduce imprudent wagering or abstain altogether. We concluded that excessive delay in seeking professional treatment services may be related to the combined influence of person and environmental factors. Specifically, the gap between needing help and obtaining help might be widened by: (a) unrealistic self-perceptions involving belief in personal luck and (b) involvement with wagering games that emphasize elements of skill.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".