Empowering senior medical residents as resuscitation team leaders
Bibliographic record
Abstract
BACKGROUND: A code blue is a medical emergency indicating a patient requiring immediate attention with a systematic hospital wide response handled in a team based approach. In academic hospital settings, medical trainees are first responders to code blues. As first responders, a senior resident is required to assume the code blue leader role. AIM: This study explores what non-technical characteristics define a code blue leader to be a good leader? METHODS: The study took place at the Northern Ontario School of Medicine. A qualitative methodology was applied. Semi-structured interviews were conducted sequentially. RESULTS: Ten senior residents were interviewed using semi-structured interviews. Three distinct themes emerged, namely: individual factors, factors influencing team work and organization factors. Results confirm that residents lack confidence in leading code blue teams. This feeling is influenced by personal, team and situational factors. Residents lack training in non-technical skills and as a result feel they don't know how to lead a code blue team and feel they lack the necessary skills to work effectively in a code blue team, especially under pressure. CONCLUSIONS: These data suggest that the lack of devoted training to non-technical skills, influences resident confidence, comfort, preparedness and functioning of resuscitative teams. Northern Ontario School of Medicine University REB approved, file number 6,021,198.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".