The Development and Evaluation of Personalized Training in Shared Decision-making Skills for Rheumatologists
Bibliographic record
Abstract
OBJECTIVE: Many factors influence a patient's preference in engaging in shared decision making (SDM). Several training programs have been developed for teaching SDM to physicians, but none of them focused on the patients' preferences. We developed an SDM training program for rheumatologists with a specific focus on patients' preferences and assessed its effects. METHODS: A training program was developed, pilot tested, and given to 30 rheumatologists. Immediately after the training and 10 weeks later, rheumatologists were asked to complete a questionnaire to evaluate the training. Patients were asked before and after the training to complete a questionnaire on patient satisfaction. RESULTS: Ten weeks after the training, 57% of the rheumatologists felt they were capable of estimating the need of patients to engage in SDM, 62% felt their communication skills had improved, and 33% reported they engaged more in SDM. Up to 268 patients were included. Overall, patient satisfaction was high, but there were no statistically significant differences in patient satisfaction before and after the training. CONCLUSION: The training was received well by the participating rheumatologists. Even in a population of rheumatologists that communicates well, 62% reported improvement. The training program increased awareness about the principles of SDM in patients and physicians, and improved physicians' communicative skills, but did not lead to further improvement in patients' satisfaction, which was already high.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".