Does the age of acute care physicians impact their (1) crisis management performance and (2) learning after simulation-based education? A protocol for a multicentre prospective cohort study in Toronto and Ottawa, Canada
Bibliographic record
Abstract
INTRODUCTION: The proportion of older acute care physicians (ACPs) has been steadily increasing. Ageing is associated with physiological changes and prospective research investigating how such age-related physiological changes affect clinical performance, including crisis resource management (CRM) skills, is lacking. There is a gap in the literature on whether physician's age influences baseline CRM performance and also learning from simulation. We aim to investigate whether ageing is associated with baseline CRM skills of ACPs (emergency, critical care and anaesthesia) using simulated crisis scenarios and to assess whether ageing influences learning from simulation-based education. METHODS AND ANALYSIS: This is a prospective cohort multicentre study recruiting ACPs from the Universities of Toronto and Ottawa, Canada. Each participant will manage an advanced cardiovascular life support crisis-simulated scenario (pretest) and then be debriefed on their CRM skills. They will then manage another simulated crisis scenario (immediate post-test). Three months after, participants will return to manage a third simulated crisis scenario (retention post-test). The relationship between biological age and chronological age will be assessed by measuring the participants CRM skills and their ability to learn from high-fidelity simulation. ETHICS AND DISSEMINATION: This protocol was approved by Sunnybrook Health Sciences Centre Research Ethics Board (REB Number 140-2015) and the Ottawa Health Science Network Research Ethics Board (#20150173-01H). The results will be disseminated in a peer-reviewed journal and at scientific meetings. TRIAL REGISTRATION NUMBER: NCT02683447; Pre-results.
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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 | 0.042 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".