A Simulation-Based Workshop to Improve Residents' Collaborative Clinical Practice
Bibliographic record
Abstract
BACKGROUND: The Accreditation Council for Graduate Medical Education expects residents to attain competency in systems-based practice by advocating for quality patient care, working in interprofessional teams, and implementing system solutions to prevent errors. Diabetes in pregnancy was identified as an area for improvement through comprehensive interdisciplinary and interprofessional care. OBJECTIVE: An interdisciplinary and interprofessional workshop was created by 3 regional academic institutions to improve collaborative practice, clinical knowledge, and clinical judgment of residents. METHODS: A workshop consisting of 4 clinical simulation stations for ultrasound assessment, glycemic control, hyperglycemic emergencies, and macrosomia complications was designed to address gaps in quality of care. Workshop participants were residents from 6 programs and students in nursing, pharmacy, and sonography. Attitude and clinical knowledge were measured preworkshop and postworkshop, and at 3-month and 6- to 7-month follow-up. RESULTS: There were increases in average clinical knowledge scores across time points from residents: 56.4% preworkshop, 64.8% postworkshop, 66.0% at 3-month follow-up, and 68.1% at 6- to 7-month follow-up. Additionally, participants reported positive attitudes toward interprofessional education and indicated high overall satisfaction. CONCLUSIONS: Residents demonstrated improved knowledge and attitudes toward interprofessional training after participating in a large-scale simulation workshop focused on the care of patients with diabetes in pregnancy.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".