Understanding dropout during psychological treatment for gambling: A scoping review
Bibliographic record
Abstract
Abstract This scoping review broadly aims to identify recent research on six indices of dropout during face-to-face psychological treatment for harmful gambling, including definitions of dropout, estimates of dropout, reasons for dropout, predictors of dropout, consequences of dropout, and solutions to dropout. A systematic search of electronic databases and grey literature identified 66 studies (from 67 articles/reports) published from 2004, most commonly contributing data to estimates (94 %), followed by predictors (74 %), definitions (73 %), reasons (15 %), consequences (8 %), and solutions (3 %). The findings revealed several definitions of dropout, typically relating to the non-attendance of a pre-defined and usually arbitrary number of treatment sessions, which risks the misclassification of clients with symptom improvement. The median rate of dropout (derived from 76 estimates) was 35.4 % across all treatment types and 34.8 % across Cognitive Behaviour Therapy (CBT) treatments. The most reported reasons for dropout were practical issues (e.g., scheduling conflicts), with few reasons relating to specific therapeutic orientations recorded. A large number of potential predictors have been examined with few consistent results, whereby being married/de-facto appears to consistently lower the risk of dropout. Dropout was consistently associated with higher subsequent gambling symptoms, urges, cognitions, and behaviors, with some additional evidence for worsened psychological symptoms, albeit based on a small number of studies. Only two studies examined the addition of motivational procedures to redress the risk of dropout, which produced favourable results. More research is needed, utilising standardised definitions and bespoke large-scale examinations, to improve retention during gambling treatment. • Broad review of research on dropout during face-to-face psychological treatment for gambling. • Dropout typically defined by non-attendance of arbitrary number of treatment sessions. • Median rate of dropout (across 76 estimates) was 35.4 % across treatment types. • Married/de-facto status consistently predicted dropout; dropout predicted worsened gambling. • Lack of research on solutions to dropout; some evidence for motivational techniques
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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 | 0.030 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".