Hospital Resilience in Three COVID-19 Referral Hospitals in Brazil
Bibliographic record
Abstract
Health crises, such as the COVID-19 pandemic, challenge health systems in demonstrating resilience-the ability to cope with change, manage challenges, and adapt in order to retain their effectiveness. Understanding how such challenges affect and produce reactions in those involved in this response is extremely important. This study evaluated resilience in three referral hospitals in the city of Recife, Pernambuco, Brazil-one public, one private, and one philanthropic hospital-by examining the coping activities adopted by the nursing staff working on the COVID-19 frontline. A multiple case study was carried out, using a qualitative approach, triangulating data from direct observations, document analysis, and interviews with 21 nursing professionals working in management and care provision. Data were collected from April to October 2020. The interviews were transcribed and analyzed based on the resilience categories defined by Blanchet (2017): absorption capacity, adaptive capacity, and transformative capacity. Four themes were considered relevant to the objectives of this study: institutional support, access to personal protective equipment (PPE), work relationships, and fear and mental health. Adaptive capacity was demonstrated concerning the four themes analyzed, absorption capacity was demonstrated in two themes, and no transformative capacity was identified. The study highlighted that the health crisis was challenging for all the hospitals studied, regardless of their legal-administrative status. No differences were observed among them in terms of resilience.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".