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Record W2626467388 · doi:10.1108/ijes-01-2017-0002

An alternative model of pre-hospital care for 999 patients who require non-emergency medical assistance

2017· article· en· W2626467388 on OpenAlexaff
Joanna M. Blodgett, Duncan Robertson, David Ratcliffe, Kenneth Rockwood

Bibliographic record

VenueInternational Journal of Emergency Services · 2017
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsDalhousie University
Fundersnot available
KeywordsReferralWork (physics)OriginalityHealth careMedical emergencyScheme (mathematics)MedicineComputer scienceNursingOperations managementPsychologyEngineeringPolitical science

Abstract

fetched live from OpenAlex

Purpose With the increasing demand on ambulance services, paramedics are tasked to arrange as much out of hospital care as possible, to develop integrated systems of care and work with hundreds of different providers – all in the 15 minutes allocated for assessment. A UK ambulance trust is navigating and leading much of this work as one of the first trusts to implement a general practitioner referral policy as an alternate to direct conveyance. The paper aims to discuss this issue. Design/methodology/approach Here the authors discuss the referral scheme, examine the limited evidence available and discuss what is needed to influence prospective success of implementing this scheme in other trusts. Findings Limited evidence for these schemes are described, however there is a clear gap in critical appraisal and methodologically rigorous evidence needed to implement these schemes in other ambulance schemes. Originality/value In order to facilitate collaboration of healthcare services and to minimize the burden of increasing numbers of patients, communication and discussion of alternate routes of care is crucial. This viewpoint piece is one of the first to emphasize the potential benefits of such schemes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0020.000
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 head0.041
GPT teacher head0.468
Teacher spread0.427 · 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 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

Citations10
Published2017
Admission routes1
Has abstractyes

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