Training gamblers to re-think their gambling choices: How contextual analytical thinking may be useful in promoting safer gambling
Bibliographic record
Abstract
BACKGROUND AND AIMS: Harmful gambling has been associated with the endorsement of fallacious cognitions that promote excessive consumption. These types of beliefs stem from intuitively derived assumptions about gambling that are fostered by fast-thinking and a lack of objective, critical thought. The current paper details an experiment designed to test whether a four-week online intervention to strengthen contextual analytical thinking in gamblers is effective in changing gamblers cognitions and encouraging safer gambling consumption. METHODS: Ninety-four regular gamblers who reported experiencing gambling-related harm were randomly allocated to either an experimental (n = 46) or control condition (n = 48), including 45 males, ranging from 19 to 65 years of age (M = 36.61; SD = 9.76). Following baseline measurement of gambling beliefs and prior week gambling consumption, participants in the experimental condition were required to complete an adaption of the Gamblers Fallacy Questionnaire designed to promote analytical thinking by educating participants on common judgement errors specific to gambling once a week for four weeks. Post-intervention measures of beliefs and gambling consumption were captured in week five. RESULTS: The experimental condition reported significantly fewer erroneous cognitions, greater endorsement of protective cognitions, and reduced time spent gambling post-intervention compared to baseline. The control group also reported a reduction in cognitions relating to predicting and controlling gambling outcomes. CONCLUSION: Cognitive interventions that encourage gamblers to challenge gambling beliefs by reflecting on gambling involvement and promoting critical thinking may be an effective tool for reducing the time people invest in gambling activities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".