Training family physicians and residents in family medicine in shared decision making to improve clinical decisions regarding the use of antibiotics for acute respiratory infections: protocol for a clustered randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: To explore ways to reduce the overuse of antibiotics for acute respiratory infections (ARIs), we conducted a pilot clustered randomized controlled trial (RCT) to evaluate DECISION+, a training program in shared decision making (SDM) for family physicians (FPs). This pilot project demonstrated the feasibility of conducting a large clustered RCT and showed that DECISION+ reduced the proportion of patients who decided to use antibiotics immediately after consulting their physician. Consequently, the objective of this study is to evaluate, in patients consulting for ARIs, if exposure of physicians to a modified version of DECISION+, DECISION+2, would reduce the proportion of patients who decide to use antibiotics immediately after consulting their physician. METHODS/DESIGN: The study is a multi-center, two-arm, parallel clustered RCT. The 12 family practice teaching units (FPTUs) in the network of the Department of Family Medicine and Emergency Medicine of Université Laval will be randomized to a DECISION+2 intervention group (experimental group) or to a no-intervention control group. These FPTUs will recruit patients consulting family physicians and residents in family medicine enrolled in the study. There will be two data collection periods: pre-intervention (baseline) including 175 patients with ARIs in each study arm, and post-intervention including 175 patients with ARIs in each study arm (total n = 700). The primary outcome will be the proportion of patients reporting a decision to use antibiotics immediately after consulting their physician. Secondary outcome measures include: 1) physicians and patients' decisional conflict; 2) the agreement between the parties' decisional conflict scores; and 3) perception of patients and physicians that SDM occurred. Also in patients, at 2 weeks follow-up, adherence to the decision, consultation for the same reason, decisional regret, and quality of life will be assessed. Finally, in both patients and physicians, intention to engage in SDM in future clinical encounters will be assessed. Intention-to-treat analyses will be applied and account for the nested design of the trial will be taken into consideration. DISCUSSION: DECISION+2 has the potential to reduce antibiotics use for ARIs by priming physicians and patients to share decisional process and empowering patients to make informed, value-based decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.207 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".