Associations between financial gambling motives, gambling frequency and level of problem gambling: a meta‐analytic review
Bibliographic record
Abstract
BACKGROUND AND AIMS: Money is central to psychological definitions of gambling, but contemporary accounts are ambiguous regarding the role of financial motives in disordered gambling. The aims of the current research were to obtain meta-analytic weighted effect sizes for zero-order associations of financial motives against gambling frequency and level of problem gambling, as well as partial associations after controlling for other motives (e.g. coping). METHODS: A meta-analysis of the literature through February 2021 was undertaken. Studies were identified from multiple sources (e.g. database search, other researchers). PRISMA standards were followed when screening identified records and extracting relevant data. The data analytic plan was pre-registered. We included 44 cross-sectional studies that involved student, community and clinical samples of people who gamble (sample sizes ranged from 22-5666), using validated self-report measures of financial gambling motives alongside measures of either gambling frequency and/or problem gambling. RESULTS: Financial gambling motives were positively associated with gambling frequency, r = 0.29, 95% CI = [0.21, 0.37], n = 22 738, and level of problem gambling, r = 0.35, 95% CI = [0.31, 0.38], n = 38 204, with moderate effect sizes. Partial associations after controlling for overlapping variance with other gambling motives were also positive (gambling frequency: β = 0.14, 95% CI = [0.05, 0.22], n = 13 844; level of problem gambling: β = 0.18, 95% CI = [0.13, 0.22], n = 28 146), with small-to-moderate effect sizes. Effect sizes were heterogeneous and the extent of heterogeneity was high. Analyses of the zero-order association involving gambling frequency indicated that gambling motives measure (greater for Gambling Motives Questionnaire-Financial) and sample mean age (greater for younger samples) were moderators. No other moderators were statistically significant. CONCLUSIONS: Financial gambling motives appear to be reliably and positively associated with both gambling frequency and level of problem 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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.021 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".