General and Gambling-Specific Types of Control: Extending Mental Health Theory and Concepts to Problem Gambling
Bibliographic record
Abstract
Rationale: A key factor in our understanding of problem gambling is control: over gambling outcomes (illusion of control) and behaviours (gambling self-efficacy). Research in the gambling field rarely looks beyond these gambling-specific types of control to more general types when identifying predictors of gambling problems. This work begins to integrate control concepts from the mental health and problem gambling fields by examining the importance of a more general type of control from the Stress Process Model: sense of control over life events. Methods: Closed-ended questionnaire and open-ended interview responses from 30 frequent (weekly or more) gamblers were used to examine whether general and gambling-specific types of control are linked as predicted in a conceptual model of control. Results: For some people, beliefs about one type of control are extended to inform beliefs about another type of control. In many cases, understandings of outcomes in life inform beliefs about controlling gambling outcomes and behaviours. Conclusions: Different types of control work together, and general understandings can translate into gambling-specific beliefs. Future work is needed to confirm and specify these relationships and clarify their importance to understanding the development of gambling problems.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.009 |
| 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".