Preferences for evidence‐based practice dissemination in addiction agencies serving women: a discrete‐choice conjoint experiment
Bibliographic record
Abstract
AIM: To model variables influencing the dissemination of evidence-based practices to addiction service providers and administrators. DESIGN: A discrete-choice conjoint experiment. We systematically varied combinations of 16 dissemination variables that might influence the adoption of evidence-based practices. Participants chose between sets of variables. SETTING: Canadian agencies (n = 333) providing addiction services to women. PARTICIPANTS: Service providers and administrators (n = 1379). MEASUREMENTS: We estimated the relative importance and optimal level of each dissemination variable. We used latent class analysis to identify subsets of participants with different preferences and simulated the conditions under which participants would use more demanding professional development options. FINDINGS: Three subsets of participants were identified: outcome-sensitive (52%), process-sensitive (29.6%) and demand-sensitive (18.2%). Across all participants, the number of clients who were expected to benefit from an evidence-based practice exerted the most influence on dissemination choices. If a practice was seen as feasible, co-worker and administrative support influenced decisions. Client benefits were most important to outcome-sensitive participants; type of dissemination process (e.g. active versus passive learning) was more important to process-sensitive participants. Brief options with little follow-up were preferred by demand-sensitive participants. Simulations predicted that initiatives selected and endorsed by government funders would reduce participation. CONCLUSIONS: Clinicians and administrators are more likely to adopt evidence-based addiction practices if the practice is seen as helpful to clients, and if it is supported by co-workers and program administration.
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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 | 0.039 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".