The impact of <scp>DECISION</scp>+2 on patient intention to engage in shared decision making: secondary analysis of a multicentre clustered randomized trial
Bibliographic record
Abstract
BACKGROUND: Training health professionals in shared decision making (SDM) may influence their patients' intention to engage in SDM. OBJECTIVE: To assess the impact of DECISION+2, a SDM training programme for family physicians about the use of antibiotics to treat acute respiratory infections (ARIs), on their patients' intention to engage in SDM in future consultations. DESIGN: Secondary analysis of a multicentre clustered randomized trial. SETTING AND PARTICIPANTS: Three hundred and fifty-nine patients consulting family physicians about an ARI in nine family practice teaching units (FPTUs). INTERVENTION: DECISION+2 (two-hour online tutorial, two-hour workshop, and decision support tools) was offered in the experimental group (five FPTUs, 162 physicians, 181 patients). Usual care was provided in the control group (four FPTUs, 108 physicians, 178 patients). OUTCOME MEASURE: Change in patients' intention scores (range -3 to +3) between pre- and post-consultation. RESULTS: The mean ± SD [median] scores of intention to engage in SDM were high in both study groups before consultation (DECISION+2 group: 1.4 ± 1.0 [1.7]; control group: 1.5 ± 1.1 [1.7]) and increased in both groups after consultation (DECISION+2 group: 2.1 ± 1.1 [2.7]; control group: 1.9 ± 1.2 [2.3]). Change of intention, classified as either increased, stable or decreased, was not statistically associated with the exposure to the DECISION+2 programme after adjusting for the cluster design (proportional odds ratio = 1.5; 95% confidence interval = 0.8-3.0). CONCLUSION: DECISION+2 had no significant impact on patients' intention to engage in SDM for choosing to use antibiotics or not to treat an ARI in future consultations. Patient-targeted interventions may be necessary to achieve this purpose.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".