Measuring treatment outcomes in gambling disorders: a systematic review
Bibliographic record
Abstract
BACKGROUND AND AIMS: Considerable variation of outcome variables used to measure recovery in the gambling treatment literature has precluded effective cross-study evaluations and hindered the development of best-practice treatment methodologies. The aim of this systematic review was to describe current diffuse concepts of recovery in the gambling field by mapping the range of outcomes and measurement strategies used to evaluate treatments, and to identify more commonly accepted indices of recovery. METHODS: A systematic search of six academic databases for studies evaluating treatments (psychological and pharmacological) for gambling disorders with a minimum 6-month follow-up. Data from eligible studies were tabulated and analysis conducted using a narrative approach. Guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) were adhered to. RESULTS: Thirty-four studies were reviewed systematically (RCTs = 17, comparative designs = 17). Sixty-three different outcome measures were identified: 25 (39.7%) assessed gambling-specific constructs, 36 (57.1%) assessed non-gambling specific constructs, and two instruments were used across both categories (3.2%). Self-report instruments ranged from psychometrically validated to ad-hoc author-designed questionnaires. Units of measurement were inconsistent, particularly in the assessment of gambling behaviour. All studies assessed indices of gambling behaviour and/or symptoms of gambling disorder. Almost all studies (n = 30; 88.2%) included secondary measures relating to psychiatric comorbidities, psychological processes linked to treatment approach, or global functioning and wellbeing. CONCLUSIONS: In research on gambling disorders, the incorporation of broader outcome domains that extend beyond disorder-specific symptoms and behaviours suggests a multi-dimensional conceptualization of recovery. Development of a single comprehensive scale to measure all aspects of gambling recovery could help to facilitate uniform reporting practices across the field.
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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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".