Lessons Learned from Field Experiences on Hospitals’ Resilience to the COVID-19 Pandemic: A Systematic Approach
Bibliographic record
Abstract
In this concluding article of the special issue, we examine lessons learned from hospitals' resilience to the COVID-19 pandemic in Brazil, Canada, France, Japan, and Mali. A quality lesson learned (QLL) results from a systematic process of collecting, compiling, and analyzing data derived ideally from sustained effort over the life of a research project and reflecting both positive and negative experiences. To produce QLLs as part of this research project, a guide to their development was drafted. The systematic approach we adopted to formulate quality lessons, while certainly complex, took into account the challenges faced by the different stakeholders involved in the fight against the COVID-19 pandemic. Here we present a comparative analysis of the lessons learned by hospitals and their staff with regard to four common themes that were the subject of empirical analyses: 1) infrastructure reorganization; 2) human resources management; 3) prevention and control of infection risk; and 4) logistics and supply. The lessons learned from the resilience of the hospitals included in this research indicate several factors to consider in preparing for a health crisis: 1) strengthening the coordination and leadership capacities of hospital managers and health authorities; 2) improving communication strategies; 3) strengthening organizational capacity; and 4) adapting resources and strategies, including for procurement and infection risk management.
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
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 | 0.156 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| 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".