Childhood use of coin pusher and crane grab machines, and adult gambling: A conceptual replication of Newall et al. (2021)
Bibliographic record
Abstract
Background and aims: Youth gambling research mainly focuses on the illegal use of age-restricted machines, but coin pusher and crane grab machines are gambling machines that can be used by people of any age in the UK, and are also in use internationally. Previous cross-sectional evidence has associated recollected childhood usage of these machines with adult gambling participation and levels of problem gambling amongst adult gamblers. We attempted to conceptually replicate the findings of one of these studies (Newall et al., 2021), while addressing some limitations of that study. Methods: A cross-sectional survey of 2,000 UK-based and -born participants aged 19-24 years. The measures were participants' recollected usage of coin pusher and crane grab machines as a child, whether they had gambled in the past 12-months or not, and the PGSI for past 12-month gamblers. Results: Overall, 5 of 7 tested associations were significant and in the hypothesized direction. Logistic regression models showed that adult gamblers were more likely to recollect using, and used at higher levels of frequency, coin pusher and crane grab machines, than non-gamblers. Then, negative binomial regression analysis showed that adults who recollected using crane grab machines at higher levels of frequency showed more gambling-related problems. Discussion and Conclusions: These results suggest that childhood usage of coin pusher and crane grab machines may act as an underappreciated risk factor for the development of gambling-related harm across the lifespan. This information may be considered for further youth gambling research and policy.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".