Surviving the storm: The key to cyber resilience and incident response in healthcare
Bibliographic record
Abstract
This article underscores the significance of cyberdefences and response processes in healthcare, highlighting their contribution to cyber resilience through adherence to industry best practices. It emphasizes the value of hypothetical scenarios as a common practice in the field to validate the effectiveness of cyber resilient actions, systems, processes, and decision-making in the face of various cyberthreats. Focusing on the ransomware threat, the provided scenario examines its impact on healthcare systems and frontline support staff, while highlighting the time-sensitive challenges faced by response teams striving to restore essential services. Furthermore, it suggests replicating such analyses with key hospital personnel to precisely assess the impact of other types of cyberthreats, such as those originating from malicious insiders or technical data breaches facilitated through social engineering attacks. By doing so, healthcare organizations can develop comprehensive and cyber resilient responses to safeguard their operations.
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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".