ConGam-PS: developing and evaluating a measurement tool of treatment providers’ views about contingency management for gambling
Bibliographic record
Abstract
Contingency management (CM) is an evidence-based behavioral intervention highly effective at promoting behavior change. Despite evidence of its efficacy, the extension of CM to the treatment of harmful gambling has been slow. Wider dissemination of CM may be facilitated through identification of perceived obstacles and barriers. The present study developed items for a new scale, the Contingency Management for Gambling Provider Survey (ConGam-PS), to measure the views of gambling treatment providers of CM for gambling. In a mixed methods (qualitative and quantitative) based approach, N = 111 UK gambling treatment providers were surveyed about their positive, negative, and neutral beliefs about CM. Descriptive analyses found that participants were open to using and receiving training in CM, and supported research on CM for treatment of gambling. Common concerns involved the potential negative consequences for clients when incentives are withdrawn and the feasibility of objectively verifying gambling abstinence. No significant associations were found between participant characteristics and CM beliefs. Overall, there is openness toward CM among treatment providers and further research and evaluation of CM for harmful gambling is warranted.
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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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".