A framework for reporting outcomes in problem gambling treatment research: the Banff, Alberta Consensus
Bibliographic record
Abstract
OBJECTIVE: The objective is provide a framework concerning the minimum features of reporting efficacy of treatment in the problem gambling field. Research to date has not used uniform outcome measures and it is, therefore, difficult to compare the relative efficacy of various approaches. Some studies emphasize self-reported behavioural measures such as frequency and intensity of gambling whereas others emphasise change with respect to the criteria used to diagnose problem gambling or use composite measures of symptom severity in multiple domains involving gambling-related thoughts, urges, and behaviours. METHODS: An expert panel consensus. RESULTS: The proposed minimum features of reporting the efficacy of treatment outcome studies are: measures of gambling behaviour - the net expenditure each month, the frequency (in days per month) with which gambling takes place, and the time spent thinking about or engaged in the pursuit of gambling each month; measures of the problems caused by gambling - especially problems in the areas of personal health, relationships, financial, and legal; these measures can be complemented by additional measures of quality of life. measures of the processes of change - whatever mechanisms of change are assumed to occur. CONCLUSIONS: We believe that these guidelines are broad enough to allow clinical research conducted from diverse perspectives to allow valid cross study evaluations of intervention studies. Such conditions will facilitate the development of empirically validated best practice guidelines for use by clinicians in the management of problem gambling.
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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 | 0.569 | 0.461 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.030 | 0.025 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.025 | 0.019 |
| Research integrity | 0.020 | 0.037 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".