Resilience engineering in practice: Reflecting on a pediatric hospital’s preparation for unknown coronavirus outbreak
Bibliographic record
Abstract
At the start of 2020, hospitals around the world were trying to adapt during the COVID-19 pandemic. From the resilience engineering perspective, this outbreak would be a significant test as healthcare institutions try to tolerate and manage this major disruption. This paper shares insights on what a stand-alone paediatric hospital in Singapore had done to stay ahead since the beginning of the outbreak. Observations were conducted from 25-Jan-20 to 25-Mar-20 to capture evidence of resilient behavior, notably in the form of improvisations. Findings revealed adaptations made across various organization levels: at the macrosystem to create capacity to isolate safely, at the mesosystem to facilitate teamwork, and at the microsystem to manage compromises at the frontlines. Juxtaposing this episode with other examples of organizational resilience, this paper maps out common resilience engineering themes in the hospital’s response to COVID-19, but also questions what defines an organization’s success in being resilient.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".