A scoping review of experimental manipulations examining the impact of monetary format on gambling behaviour
Bibliographic record
Abstract
Gambling involves monetary bets and prizes, but the money can take a range of formats, including cash, chips, ticket-in ticket-out vouchers, and digital options including banking cards. As societies move towards cashless payment for many goods, the question arises of how emerging payment technologies might impact gambling related harms. We performed a scoping review following PRISMA guidelines to identify research testing the effects of monetary format in gambling. Our eligibility criteria focused on controlled experimental manipulations, to best establish the causal impact of monetary format. We sought to characterize different types of monetary manipulations that have been studied in a gambling context. We identified 15 eligible articles, comprising 18 individual experiments. These experiments were organized according to four distinct manipulations. The most common design (11 experiments), compared gambling under the presence or absence of money. Smaller numbers of experiments were identified manipulating monetary salience, testing Responsible Gambling tools, and testing the impact of promotional inducements. We identified no studies that compared gambling using cash against alternative payment forms. Our review highlights a paucity of research testing the possible impact of digital and cashless payment options on gambling related harms, using experimental designs that would permit causal conclusions to be drawn.
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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".