Hospital Governance During the COVID-19 Pandemic: A Multiple-Country Case Study
Bibliographic record
Abstract
In response to the disruptions caused by COVID-19, hospitals around the world proactively or reactively developed and/or re-organized their governance structures to manage the COVID-19 response. Hospitals' governance played a crucial role in their ability to reorganize and respond to the pressing needs of their staff. We discuss and compare six hospital cases from four countries on different continents: Brazil, Canada, France, and Japan. Our study examined how governance strategies (e.g., special task forces, communications management tools, etc.) were perceived by hospital staff. Key findings from a total of 177 qualitative interviews with diverse hospital stakeholders were analyzed using three categories drawn from the European Observatory on Health Systems and Policies framework on health systems resilience during the COVID-19 pandemic: 1) delivering a clear and timely COVID-19 response strategy; 2) coordinating effectively within (horizontally) and across (vertically) levels of decision-making; and 3) communicating clearly and transparently with the hospital's diverse stakeholders. Our study gleaned rich accounts for these three categories, highlighting significant variations across settings. These variations were primarily determined by the hospitals' environment prior to the COVID-19 crisis, namely whether there already existed a culture of managerial openness (including spaces for social interactions among hospital staff) and whether preparedness planning and training had been routinely integrated into their activities.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".