Exploring residents’ spontaneous collaborative skills in a simulated setting context: an exploratory study on CanMEDS collaborator role
Bibliographic record
Abstract
Kathleen Ouellet,1 Robert Sabbagh,2 Linda Bergeron,3 Sandeep Kumar Mayer,2 Christina St-Onge4 1Center for Health Profession Education, Faculty of Medicine and Health Sciences, Université de Sherbrooke, 2Department of Surgery, Faculty of Medicine and Health Sciences, Université de Sherbrooke, 3Research Chair in Medical Education, Paul Grand’Maison of the Société des médecins, Université de Sherbrooke, 4Department of Medicine, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada Background: Collaboration is an important competence to be acquired by residents. Although improving residents’ collaboration via interprofessional education has been investigated in many studies, little is known about the residents’ spontaneous collaborative behavior. The purpose of this exploratory study was to describe how residents spontaneously collaborate.Methods: Seven first-year residents (postgraduate year 1; three from family medicine and one each from ear, nose, and throat, obstetrics/gynecology, general surgery, and orthopedic surgery) participated in two collaborative meetings with actors performing the part of other health professionals (ie, occupational therapist, physiotherapist, nurse, or social worker). Both meetings were built around an issue or conflict with the patients’ families reported by one professional. The residents were required to lead the meeting to collect proper information to reach a joint decision. Two team members analyzed the video recordings of the meetings using an emerging-theme qualitative methodology.Results: Although the residents spontaneously knew how to successfully communicate with other professionals, they seemed to struggle with the patient-centered approach and the shared decision-making process.Discussion: Even if the residents performed communication-wise in their collaborative role, they seemed to have perceived themselves as decision makers instead of collaborators in the joint decision process. The results of this study can inform future studies on learning strategies to improve behaviors that would more likely need attention in interprofessional education. Keywords: interprofessional education, IPE, communication, collaboration
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".