Losing sight of Luck: Automatic approach tendencies toward gambling cues in Canadian moderate- to high-risk gamblers – A replication study
Bibliographic record
Abstract
Evidence for approach bias tendencies to underly automatic behavioural impulses towards seeking out gambling activities in the presence of appetitive salient cues was first shown by Boffo et al. (2018) in a Dutch sample. Relative to non-problem gamblers, moderate-to-high-risk gamblers demonstrated stronger approach tendencies towards gambling-related stimuli compared with neutral ones. Moreover, gambling approach bias was associated with past-month gambling behaviour and predictive of gambling activity persistence over time. The current study aimed to replicate these findings within a Canadian sample evaluating the concurrent and longitudinal correlates of gambling approach bias. The study was conducted online, available throughout Canada. Twenty-seven non-treatment-seeking moderate-to-high-risk gamblers and 26 non-problem gamblers community-recruited via multiple channels (i.e., internet and newspaper advertisements, land-based flyers, and university recruitment portals). Participants completed two online assessment sessions 6-months apart. Each session included (1) self-report measures of gambling behaviour (frequency, duration, and expenditure), (2) self-report assessment of problem gambling severity (PGSI), and (3) a gambling approach-avoidance task, utilising culturally relevant stimuli tailored to individual gambling habits. However, our study failed to replicate Boffo et al. (2018) findings in a Canadian sample. Relative to non-problem gamblers, moderate-to-high-risk gamblers did not exhibit greater approach bias tendencies towards gambling-related stimuli compared to neutral stimuli. Moreover, gambling approach bias was not predictive of prospective gambling behaviour (frequency, duration, or expenditure) or severity of gambling problems. Reported results do not provide evidence for approach tendencies contributing to problematic gambling behaviour in a Canadian sample of moderate-to-high-risk gamblers compared to non-problematic gambler controls. Further replications on the topic are needed. Future research should evaluate approach tendencies within the gambling context, considering the potential impact of task reliability to assess approach bias in light of individual gambling modality preferences.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".