Examining personalized feedback interventions for gambling disorders: A systematic review
Bibliographic record
Abstract
Background and aims Personalized feedback interventions (PFI) have shown success as a low-cost, scalable intervention for reducing problematic and excessive consumption of alcohol. Recently, researchers have begun to apply PFI as an intervention method for problematic gambling behaviors. A systematic review of the literature on PFI as an intervention/prevention method for gambling behaviors was performed. Methods Studies were included if they met the following criteria: the design included both a PFI group and a comparison group, and the interventions focused on gambling prevention and/or reduction. Six relevant studies were found meeting all criteria. Results Results revealed that PFI treatment groups showed decreases in a variety of gambling behaviors as compared to control groups, and perceived norms on gambling behaviors significantly decreased after interventions as compared to control groups. Conclusions Overall, the research suggests that while PFI applied to gambling is still in its infancy, problematic gamblers appear to benefit from programs incorporating PFIs. Further, PFI may also be used as a promising source of preventative measures for individuals displaying at-risk gambling behaviors. While, evidence is still limited, and additional research needs to be conducted with PFI for gambling problems, the preliminary positive results along with the structure of PFI as a scalable and relatively inexpensive intervention method provides promising support for future studies.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".