Attitudes Toward Evidence-Based Practices and Their Influence on Beliefs about Contingency Management: A Survey of Addiction Treatment Providers Across Canada
Bibliographic record
Abstract
Contingency management (CM) is an evidence-based treatment for addictive disorders that is often underused in clinical practice. The attitudes and beliefs of frontline staff are frequently reported as barriers to the uptake and use of evidence-based treatments, including CM. Understanding these barriers are an important step in implementation and could impact an intervention’s efficacy. Thus, we investigated the influence of attitudes toward evidence-based practices (EBP) on beliefs about CM. Our sample included 74 (19.58% response rate) substance use disorder treatment providers from 33 programs across six Canadian provinces. Most providers were not familiar with CM and reported largely neutral attitudes toward CM. However, providers also endorsed a desire for additional training in CM. In our multilevel modelling (MLM) analysis, we found that providers who believed that clinical experience was more important than EBPs reported more general barriers toward CM and fewer positive beliefs about CM. Providers with more openness and greater overall positive attitudes toward the adoption of EBPs were more likely to endorse positive beliefs about CM. Certain demographic characteristics were also associated with beliefs about CM. Providers in recovery from a substance use disorder reported greater barriers to adopting CM. In addition, those with higher levels of education held more positive beliefs about CM. Our findings provide evidence to support the consideration of provider-level characteristics in the implementation of EBPs in Canadian settings. Further, our results highlight the importance of integrating psychoeducation and training into implementation efforts to support the success of CM interventions in Canadian clinical settings.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".