Childhood use of coin pusher and crane grab machines, and adult gambling: Robustness to subjective confidence in a young adult USA sample
Bibliographic record
Abstract
Gambling as a youth is a risk factor for experiencing gambling-related harm as an adult. Most youth gambling research focuses on illegal engagement with age-restricted products, but youth can also gamble legally, by for example betting with friends, or via coin pusher and crane grab machines. Research has associated recollected rates of usage of these machines as a child with adult gambling participation and problems, but only in the UK and Australia, and has not tested for robustness to subjective confidence. The present study conceptually replicated these prior studies by investigating the association between recollected childhood use of coin push and crane grab machines, and adult gambling behavior, in a young adult USA sample. Participants rated their subjective confidence to test if individual differences in recollection biases provided a better account for any observed associations. Results found high recollected engagement rates for both coin pusher (87.2%) and crane grab machines (97.0%), and 5 of the 6 tested associations between youth machine usage and adult gambling engagement and problems were significant and in the hypothesized direction. Rates of subjective confidence were on average high (83.3 and 89.2 on a 0 to 100 scale), and generally did not interact with participants’ recollected rates of machine use. These findings extend prior research on potential public health concerns around children’s legal engagement with coin pusher and crane grab machines to a new country, the USA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".