Evaluation of a collaborator objective structured clinical examination (COSCE) in postgraduate medical education
Bibliographic record
Abstract
Background: Effective intra- and interprofessional collaboration abilities are necessary for safe and effective medical care, however such roles are often informally taught in postgraduate medical education with lack of opportunity for practice and feedback. The Objective Structured Clinical Encounter (OSCE) is a common approach in medical education. Adaptations of the OSCE have been found useful in the assessment of collaborator competencies amongst interprofessional student groups and assessment of intrinsic roles, like collaboration.Objective: The purpose of this study was to evaluate the effectiveness of a Collaborator Objective Structured Clinical Encounter (COSCE) as a method of formative assessment on collaborator competencies for postgraduate trainees. Methods: This study involved a one group, pretest-posttest evaluation conducted in 2018. PGY1 residents completed a Team Skills Scale immediately before and after COSCE participation, completed an evaluation survey to report satisfaction, and were assessed by facilitators and peer assessors using a COSCE rubric.Results: Residents reported significant improvement in their pre (N=35) to post-team skills (N=37) scores and an overall positive level of satisfaction with the COSCE experience (N=37/39, 94.9% response rate). Transfer of care skills (e.g., handover) demonstrated the lowest performance scores across all COSCE stations. Peer assessor (N = 204) and facilitator scores (N = 47) also indicated a moderate level of interrelatedness. Conclusion: A COSCE is a feasible method of formative assessment, fostering reflection and learning, and providing feedback on collaborator skills early in postgraduate medical education. Peer assessment may also hold promise as a formative assessment method on intra- and interprofessional collaboration.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".