A clean slate: adapting the realization effect to online gambling and its effectiveness in people with gambling problems
Bibliographic record
Abstract
Betting more after losses (i.e. ‘loss chasing’) is a central clinical feature of disordered gambling. According to Prospect Theory, increasing risk-seeking following losses could arise from a failure to ‘re-reference’. By contrast, successful re-referencing between successive decisions closes the mental account, and any losses are regarded as final or realized; gamblers should not chase realized losses. The present study sought to test this ‘realization effect’ among gamblers using an ecologically-valid online gambling task. We were further interested in whether the effectiveness of the loss realization varied as a function of problem gambling severity. Using online recruitment of past-year gamblers stratified on the Problem Gambling Severity Index, we tested a group without gambling problems (n=227), a group with at-risk gambling (n=239), and a group with gambling problems (n=223). Over a sequence of 9 bets, after bet 6, half of the participants underwent a simulated realization procedure that entailed cashing out from the gambling website, and re-depositing their remaining funds on another website. The feedback comparison group were shown their account balance after bet 6 but did not withdraw or transfer their funds. In line with the realization effect, the group with non-problem gambling significantly reduced their bet after cashing out. The realization procedure did not significantly ameliorate loss chasing in the groups with at-risk gambling or gambling problems. We conclude that the realization effect can be elicited in an online gambling context, but that stronger interventions for realizing losses may be required for people experiencing gambling problems.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".