Review of National Health Service England’s Emergency Preparedness, Resilience and Response Annual Assurance for 2021–2022
Bibliographic record
Abstract
OBJECTIVE: National Health Service (NHS) England conducts annual assurance of NHS bodies in England's readiness to respond to emergencies using its Core Standards for emergency preparedness, resilience, and response (EPRR). This review assessed whether the first complete EPRR assurance after England's coronavirus disease (COVID-19) pandemic national response was performed successfully. METHODS: The primary outcome of interest was the quantity of information regarding applicable Core Standards held by NHS England at the end of that assurance. Secondary outcomes were variations between the number of applicable Core Standards and information held by NHS bodies about the number of applicable Core Standards. RESULTS: NHS England recorded the correct number of applicable Core Standards for 88 of the 124 NHS trusts in England which provided general hospital accommodation and services in relation to accidents or emergencies. It recorded an incorrect number of standards for 13 trusts and did not record the number of standards for 23 trusts. CONCLUSION: NHS England's EPRR assurance resulted in correct data not being recorded for over a quarter of the above NHS trusts. This review may also be of interest to other state-level bodies that rely on the high-level assurance of their ability to provide health care during emergencies.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".