We need to work differently in a crisis: peer-professional leadership to redesign physicians’ work
Bibliographic record
Abstract
BACKGROUND: Understanding physician leadership is critical during pandemics and other health crises when formal organisational leaders may be unable to respond expeditiously. This study examined how physician leaders managed to quickly design a new model for acute-care physicians' work, adopted across four large hospitals in a public health authority in Canada during the COVID-19 pandemic. METHODS: The research employed a qualitative case study methodology, with inductive analysis of interview transcripts and documents. Shortly after a physician work model redesign, we interviewed key informants: the physician leaders and others who participated in or supported the model's development. Participants were chosen based on their leadership role and through snowballing. All those who were approached agreed to participate. RESULTS: A process model describes leadership actions during four phases of work model development (priming, early planning, readying for operations and transition). These actions were: (1) recognising the threat, (2) committing to action, (3) forming and organising, (4) building and relying on relationships, (5) developing supporting processes and (6) designing functions and structure. We offer three additional contributions to knowledge about leadership in a time of crisis: (1) leveraging peer-professional leadership to initiate, formalise and organise change processes, (2) designing a new work model on existing and emerging evidence and (3) building and relying on relationships to unify various actors. CONCLUSIONS: The model of peer-professional leadership can deepen understanding of how to lead professionals. Our findings could assist peer-professional and organisational leaders to encourage quick redesign of professionals' work in response to new phases of the COVID-19 pandemic or other crises.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".