Leadership in crisis situations: merging the interdisciplinary silos
Bibliographic record
Abstract
Purpose Complex clinical situations, involving multiple medical specialists, create potential for tension or lack of clarity over leadership roles and may result in miscommunication, errors and poor patient outcomes. Even though copresence has been shown to overcome some differences among team members, the coordination literature provides little guidance on the relationship between coordination and leadership in highly specialized health settings. The purpose of this paper is to determine how different specialties involved in critical medical situations perceive the role of a leader and its contribution to effective crisis management, to better define leadership and improve interdisciplinary leadership and education. Design/methodology/approach A qualitative study was conducted featuring purposively sampled, semi-structured interviews with 27 physicians, from three different specialties involved in crisis resource management in pediatric centers across Canada: Pediatric Emergency Medicine, Otolaryngology and Anesthesia. A total of three researchers independently organized participant responses into categories. The categories were further refined into conceptual themes through iterative negotiation among the researchers. Findings Relatively "structured" (predictable) cases were amenable to concrete distributed leadership - the performance by micro-teams of specialized tasks with relative independence from each other. In contrast, relatively "unstructured" (unpredictable) cases required higher-level coordinative leadership - the overall management of the context and allocations of priorities by a designated individual. Originality/value Crisis medicine relies on designated leadership over highly differentiated personnel and unpredictable events. This challenges the notion of organic coordination and upholds the validity of a concept of leadership for crisis medicine that is not reducible to simple coordination. The intersection of predictability of cases with types of leadership can be incorporated into medical simulation training to develop non-technical skills crisis management and adaptive leaderships skills.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".