Behavioural expressions of loss-chasing in gambling: A systematic scoping review
Bibliographic record
Abstract
Loss-chasing, the tendency to continue and/or intensify gambling following losses, is a key clinical symptom in gambling disorder and a central feature in problem gambling, endorsed by at-risk problem gamblers. Despite its centrality, the extant literature has often operationalised loss-chasing across distinct behavioural expressions. The current systematic scoping review aimed to map the heterogeneous operationalisations of loss-chasing in the literature. The reviewed studies defined loss-chasing either between-sessions (n=39) or within-sessions (n=38), as a long-recognised distinction. For both categories, further behavioural expressions could be distinguished. Between-session loss-chasing was captured by endorsing an item ‘returning another day/time to recoup losses’, or behaviourally as the interval between successive sessions, or as increasing stakes on the next visit. Within-session loss-chasing was defined as continuing to gamble, and/or intensifying betting either by increased risk-taking, stake size, or speed of play. Additionally, much heterogeneity was observed in gambling contexts examined, the exact definition of loss, and the potential delineation of win-chasing. Open questions and future directions are discussed. Overall, this paper severs as a first step towards more conceptual clarity of loss-chasing.
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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 | 0.010 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".