Placing your faith on the betting floor: Religiosity predicts disordered gambling via gambling fallacies
Bibliographic record
Abstract
Background and aims We examined the potential role religious beliefs may play in disordered gambling. Specifically, we tested the idea that religiosity primes people to place their faith in good fortune or a higher power. In the context of gambling, however, this may lead to gambling fallacies (e.g., erroneous beliefs that one has control over a random outcome). People who are high in religiosity may be more at risk of developing gambling fallacies, as they may believe that a higher power can influence a game of chance. Thus, this research investigated the relationship between religiosity and gambling problems and whether gambling fallacies mediated this relationship. Methods In Study 1, we recruited an online sample from Amazon's Mechanical Turk to complete measures that assessed the central constructs (religiosity, disordered gambling, and gambling fallacies). In Study 2, we conducted a secondary analysis of a large data set of representative adults (N = 4,121) from a Canadian province, which contained measures that assessed the constructs of interest. Results In Study 1, religiosity significantly predicted gambling problem. Conversely, there was no direct relationship between religiosity and gambling in Study 2. Importantly, a significant indirect effect of religiosity on disordered gambling severity through gambling fallacies was found in both studies, thus establishing mediation. The results remained the same when controlling for age, gender, ethnicity, and socioeconomic status for both studies. Discussion and conclusion These findings suggest religiosity and its propensity to be associated with gambling fallacies, which should be considered in the progression (and possibly treatment) of gambling.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".