Towards strengthening resilience of organizations by risk management tools: A scientometric perspective on COVID‐19 experience in a healthcare and industrial setting
Bibliographic record
Abstract
Abstract During the COVID‐19 pandemic, the healthcare system and the global supply chain were exposed to an unpredicted event, which increased awareness about the need of more effective strategies to support decision‐making process and to empower safety barriers. In this work, a combined scientometric and systematic review was performed to analyze tools and methodologies able to combine resilience with more traditional risk assessment, learning from the experience posed by the COVID‐19 crisis. Bibliometric and literature content analyses were carried out focusing on resilience management upon the incoming of an unexpected event. The systematic analysis of the methods and models developed on the basis of different pandemic waves provides a natural guide for future research development.
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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 | 0.058 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.111 | 0.114 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".