The Dynamics of Control: Exploring Sense of Control, Illusion of Control, and Gambling Self-efficacy among Frequent Gamblers
Bibliographic record
Abstract
Purpose: This study examines how three types of control - sense of control, illusion of control, and gambling self-efficacy - clarify the relationship between frequent gambling and gambling-related harm. Its objectives are to examine 1) how the three types of control are understood and experienced by the individual, 2) how the types of control link with each other, and 3) how the types of control help explain differences in gambling-related problems. Rationale: Central to this research is the argument that mental health and behavioural addiction theory should be integrated in order to offer a more complete understanding of health and illness. This research bridges the divide by creating and evaluating a new theoretical model. The Dynamics of Control Model incorporates types of control and relationships from the Stress Process Model of mental health and the Integrated Pathways Model of problem gambling. This study focuses on control because it is a central concept in both addiction research and the sociological study of mental health. Methods: Mixed methods are used in this research. Thirty in-depth interviews were conducted with frequent gamblers from Simcoe County, Ontario who play games of skill or chance once a week or more. These data are supplemented with secondary analysis of the 2002 Canadian Community Health Survey, a large nationally representative survey on mental health. Results: Sense of control and gambling self-efficacy help explain the relationship between gambling frequency and problem gambling severity. Frequent gambling is accompanied by little harm when the individual has high sense of control and high gambling self-efficacy. Illusion of control does not play a role in explaining problem gambling severity but is best predicted by type of game. All three types of control are more complex than described in the literature, with internal variations, thresholds of effectiveness, and conceptual limitations. Implications: This study's findings stimulate discussion on low-risk gambling behaviours and the use of categorical diagnoses. The results support future collaborations between mental health and behavioural addictions research, and increased use of the sociological perspective to examine problem gambling. The study concludes by suggesting ways of improving the conceptualization, measurement, and study of control.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".