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Record W4402692361 · doi:10.1017/dmp.2024.113

Review of National Health Service England’s Emergency Preparedness, Resilience and Response Annual Assurance for 2021–2022

2024· article· en· W4402692361 on OpenAlexaboutno aff
William Wetherell

Bibliographic record

VenueDisaster Medicine and Public Health Preparedness · 2024
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPreparednessResilience (materials science)Service (business)PandemicMedicineCoronavirus disease 2019 (COVID-19)Quarter (Canadian coin)Medical emergencyBusinessPolitical scienceGeographyDisease

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.380
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.096
GPT teacher head0.469
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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