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Record W2733045809

Adoption of the National Early Warning Score: a survey of hospital trusts in England, Northern Ireland and Wales, Canadian Association for Health Services and Policy Research

2016· article· en· W2733045809 on OpenAlexaboutno aff
Ugochi Nwulu, Jamie J. Coleman

Bibliographic record

VenueKent Academic Repository (University of Kent) · 2016
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEarly warning scoreMedicineParliamentVital signsHealth carePublic healthMedical emergencyFamily medicineWarning systemBusinessNursingPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Objectives: The primary objective was to elicit the uptake of a standardised vital signs early warning score - National Early Warning Score (NEWS) in hospitals in England, Wales and Northern Ireland. In 2012, a Royal College of Physicians’ taskforce developed Approach: A short survey was sent to 223 hospitals in July 2014. Hospitals were members of a regional critical care network and had an adult general critical care unit. The hospitals were contacted using the Freedom of Information Act (2000), an act of the United Kingdom parliament that creates a public "right of access" to information held by public authorities. Hospitals were asked if they used NEWS or had plans to adopt it, if they used electronic health records and a computerized vital signs monitoring system in their non-critical care wards. Data received from 217 of the 223 hospitals were analysed. Results: 27% of hospitals have some form of electronic health record system in their non-critical care wards and 20% of hospitals use computerized methods to record vital signs. All but one hospital uses a multiple parameter early warning score. Over half (55.5%) of hospitals use NEWS and 17% had plans to adopt it. Some hospitals wished to use it as part of an electronic health record system rollout planned for later in the financial year. Half of the hospitals which had no plans to adopt NEWS (24 of 44) gave explicit reasons as to why with the number one reason being that they already used a similar score. Absence of a parameter used in other scores (urine output) was also an issue for non-adopters of NEWS. Conclusions: The results suggest that there is a steadily increasing level of acceptance of NEWS. The increased use of electronic health records appears to have helped some hospitals to adopt NEWS. A small number of hospitals disclosed that they had adapted NEWS which is a threat to the standardisation intended.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.847

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.288
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2016
Admission routes1
Has abstractyes

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