Chasing in online gambling and its practical application in gambling intervention
Bibliographic record
Abstract
'Chasing' refers to a tendency to increase betting in an effort to recoup prior losses (i.e., 'loss chasing') or to satisfy an increased gambling desire following wins (i.e., 'win chasing'). Chasing characterizes the transition from recreational to problem gambling, but few studies have examined this behaviour systematically. Using online gambling data, I sought a holistic understanding of chasing by capturing between- and within-session chasing behaviour. Further, I evaluated a behavioural intervention to reduce loss chasing. Chapters 2, 3, and 4 examined between- and within-session chasing using naturalistic data from a gambling website (PlayNow.com in British Columbia, Canada). For between-session chasing, average gamblers returned more slowly after a losing session and returned more quickly after a winning session. Within-session chasing depends on game type, chasing measurement (bet amount vs. quit probability), and outcome timeframe (immediate vs. cumulative outcomes). Across most games, loss chasing depended more on timeframe but not measurement, whereas win chasing depended more on measurement. After losing more on the last bet, gamblers staked larger amounts over a longer session, but when cumulative losses mounted, gamblers staked smaller amounts over a shorter session. After winning more, gamblers bet more over a shorter session. Chapter 4 used enrolment in the Voluntary Self-Exclusion (VSE) program as a proxy for likely gambling problems. While conventional assessments of gambling problems focus on between-session loss chasing, it did not differentiate VSE gamblers from Non-VSE gamblers. Instead, these groups differed in their within-session loss chasing tendencies. Therefore, future research aiming to identify high-risk gambling patterns should also consider within-session behavioural markers. Chapter 5 recruited gamblers from the survey platform, Prolific, and examined the effectiveness of a novel ‘cashing out’ procedure in alleviating loss chasing. In non-problem gamblers, the cashing out manipulation significantly lowered the amount wagered, aligned with the realization effect. There was no interaction between the cashing-out condition and the gambling group, although the cashing-out procedure was not statistically significant in the at-risk or problem gambling groups. These findings provide future directions for identifying gambling problems based on behavioural tracking data, and present proof of concept data for a new digital harm reduction tool.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".