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Record W2886945711 · doi:10.1177/0840470418773416

Evolution of a high-performance emergency health services system in Nova Scotia

2018· article· en· W2886945711 on OpenAlexaffabout
Andrew H. Travers

Bibliographic record

VenueHealthcare Management Forum · 2018
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsCapital District Health AuthorityNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsNova scotiaEmergency medical servicesHealth careMedical emergencyHealthcare systemBusinessService (business)Health servicesPublic healthPopulationNursingProcess managementMedicineMarketingEnvironmental health

Abstract

fetched live from OpenAlex

Since 1997, Emergency Health Services in Nova Scotia (NS) has evolved from a program providing prehospital care for patients in transport to a system providing integrated healthcare in both traditional (ie, ambulance) and non-traditional settings (eg, patient homes, hospital settings). This article highlights (1) the reorganization of the emergency medical service system design, (2) the strategies enabling efficient operation of this design, and (3) resultant innovations evolving from both system redesign and strategy application. Emergency Health Services has utilized a Public Utility Model (PUM) design providing prehospital healthcare, public safety, and public health responses to the population of NS. The success of the PUM has been complimented by three strategies: (1) co-leadership model operations, (2) common languages to translate evidence into practice, and (3) collaborative and integrated relationships with other regulated healthcare providers. This prehospital system design and application strategies could be applied in other sectors of community and hospital systems of care.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.099
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.295
Teacher spread0.279 · 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.

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

Citations4
Published2018
Admission routes2
Has abstractyes

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