Healthcare system resiliency: The case for taking disaster plans further — Part 1
Bibliographic record
Abstract
For the most part, top management is aware of the costs of healthcare downtime. They recognise that minimising downtime while fulfilling risk management standards, namely, 'duty of care' and 'standard of care', are among the most difficult challenges they face, especially when coupled with the increasing pressure for continued service availability with the frequency of incidents. Through continuous operational availability and greater resiliency demands a new, combined approach has emerged, which necessitates that the disciplines of: (1) enterprise risk management; (2) emergency response planning; (3) business continuity management including IT disaster recovery; (4) crisis communications be addressed with strategies and techniques designed and integrated into a singular, seamless approach. It is no longer feasible to separate these disciplines. By integrating them as the gateway for service continuity, the organisation can enhance its ability to run as a business by helping to identify risks and prepare for change, prioritise work efforts, flag problems and pinpoint important areas that underpin the overarching business continuity processes. The driver of change in staying ahead of the risk curve, and the entry point of a true resiliency strategy, begins with identifying the synergies of the aforementioned disciplines and integrating each of them to jointly contribute to service continuance.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 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".