Barriers and facilitators to the dissemination of DECISION+, a continuing medical education program for optimizing decisions about antibiotics for acute respiratory infections in primary care: A study protocol
Bibliographic record
Abstract
BACKGROUND: In North America, acute respiratory infections are the main reason for doctors' visits in primary care. Family physicians and their patients overuse antibiotics for treating acute respiratory infections. In a pilot clustered randomized trial, we showed that DECISION+, a continuing medical education program in shared decision making, has the potential to reduce the overuse of antibiotics for treating acute respiratory infections. DECISION+ learning activities consisted of three interactive sessions of three hours each, reminders at the point of care, and feedback to doctors on their agreement with patients about comfort with the decision whether to use antibiotics. The objective of this study is to identify the barriers and facilitators to physicians' participation in DECISION+ with the goal of disseminating DECISION+ on a larger scale. METHODS/DESIGN: This descriptive study will use mixed methods and retrospective and prospective components. All analyses will be based on an adapted version of the Ottawa Model of Research Use. First, we will use qualitative methods to analyze the following retrospective data from the pilot study: the logbooks of eight research assistants, the transcriptions of 15 training sessions, and 27 participant evaluations of the DECISION+ training sessions. Second, we will collect prospective data in semi-structured focus groups composed of family physicians to identify barriers and facilitators to the dissemination of a future training program similar to DECISION+. All 39 family physicians exposed to DECISION+ during the pilot project will be eligible to participate. We will use a self-administered questionnaire based on Azjen's Theory of Planned Behaviour to assess participants' intention to take part in future training programs similar to DECISION+. DISCUSSION: Barriers and facilitators identified in this project will guide modifications to DECISION+, a continuing medical education program in shared decision making regarding the use of antibiotics in acute respiratory infections, to facilitate its dissemination in primary care on a large scale. Our results should help continuing medical educators develop a continuing medical education program in shared decision making for other clinically relevant topics. This will help optimize clinical decisions in primary care.
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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 | 0.056 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".