Piloting the addition of contingency management to best practice counselling as an adjunct treatment for rural and remote disordered gamblers: study protocol
Bibliographic record
Abstract
INTRODUCTION: Problematic gambling is a significant Canadian public health concern that causes harm to the gambler, their families, and society. However, a significant minority of gambling treatment seekers drop out prior to the issue being resolved; those with higher impulsivity scores have the highest drop-out rates. Consequently, retention is a major concern for treatment providers. The aim of this study is to investigate the efficacy of internet-delivered cognitive behavioural therapy (CBT) and internet-delivered CBT and contingency management (CM+) as treatments for gambling disorder in rural Albertan populations. Contingency management (CM) is a successful treatment approach for substance dependence that uses small incentives to reinforce abstinence. This approach may be suitable for the treatment of gambling disorder. Furthermore, internet-delivered CM may hold particular promise in rural contexts, as these communities typically struggle to access traditional clinic-based counselling opportunities. METHODS AND ANALYSIS: 54 adults with gambling disorder will be randomised into one of two conditions: CM and CBT (CM+) or CBT alone (CBT). Gambling will be assessed at intake, every treatment session, post-treatment, and follow-up. The primary outcome measures are treatment attendance, gambling abstinence, gambling, gambling symptomatology, and gambling urge. In addition, qualitative interviews assessing study experiences will be conducted with the supervising counsellor, graduate student counsellors, study affiliates, and a subset of treatment seekers. This is the first study to use CM as a treatment for gambling disorder in rural and remote populations. ETHICS AND DISSEMINATION: This study was approved by the University of Lethbridge's Human Subject Research Committee (#2016-080). The investigators plan to publish the results from this study in academic peer-reviewed journals. Summary information will be provided to the funder. TRIAL REGISTRATION NUMBER: NCT02953899; Pre-results.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.049 | 0.008 |
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".