A framework for reporting outcomes in problem gambling treatment research: the Banff, Alberta Consensus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it