Cash outs during in-play sports betting: Who, why, and what it reveals
Bibliographic record
Abstract
Cashing out is a popular feature of modern 'in-play' sports betting that allows sports bettors to withdraw a bet before the sporting event on which the bet was placed is finalized. Previous studies have shown that use of the cash out feature is positively related to problem gambling symptomatology. However, little is known about demographic and psychological characteristics of in-play sports bettors who use the cash out feature, or their motivations for use. To fill this knowledge gap, we recruited 224 adults (18 + years) from Ontario who engaged in in-play sports betting in the past three months. Participants completed self-report measures of psychological and gambling-related variables. Participants also provided qualitative responses for their motivations for using the cash out feature. Approximately half (51.8 %) of the participants reported using the cash out feature. No statistically significant demographic differences were found between participants who used and did not use the cash out feature. Participants who used the feature (compared to those who did not) reported higher problematic alcohol and cannabis use, feelings of depression, anxiety, and stress, and were motivated to gamble to make money. The primary reasons for cashing out were to access money immediately, to cut losses, and because cashing out felt like a less risky option. The current findings shed light on underlying psychological vulnerabilities associated with individuals who use the cash out feature, which can inform initiatives to reduce the harms associated with this popular feature of sports betting.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".