Leveraging local knowledge for crisis management: a practice-based approach to managing uncertainty in healthcare during COVID-19
Bibliographic record
Abstract
BACKGROUND/AIM: Crises like the COVID-19 pandemic are inherently uncertain, dynamic and generate broader consequences on organisations, challenging traditional crisis management approaches. Conventional approaches often neglect the mechanisms and processes frontline practitioners enact in their local practices to adapt effectively. This study explores how healthcare professionals (HPs) at a university hospital centre developed and mobilised local knowledge to rapidly respond to the evolving conditions of the COVID-19 pandemic. METHODS: We conducted an interpretive single case study at a designated COVID-19 university hospital in Montreal, Canada. Over 6 months (April to September 2020), we collected data through 49 virtual interviews with healthcare practitioners, minutes from an operational crisis unit and organisational records such as protocols and clinical algorithms. Our analysis focused on identifying spaces and mechanisms that facilitated the creation, testing and translation of local knowledge across different clinical units, leading to rapid organisational adaptation. RESULTS: The study reveals that frontline HPs enacted new mechanisms forming three types of spaces-reflective, experimental and translational-that bypassed existing organisational structures of knowledge development. These spaces enabled the rapid development and translation of local knowledge, fostering dynamic organisational responses to the evolving crisis. CONCLUSION: By highlighting the critical role of local knowledge and the processes supporting its integration, this research offers valuable insights into improving crisis management practices. It emphasises frontline practitioners' improvised and flexible organising processes that enable a more global capacity to leverage local knowledge for the effective adaptation in unprecedented crisis situations.
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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 | 0.050 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.007 | 0.029 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".