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Record W2116536464 · doi:10.1093/pubmed/fdi079

Emergency call work-load, deprivation and population density: an investigation into ambulance services across England

2006· article· en· W2116536464 on OpenAlexaff
Philip J. Peacock, Janet L. Peacock

Bibliographic record

VenueJournal of Public Health · 2006
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsInstitute of Population and Public Health
Fundersnot available
KeywordsAmbulance serviceDemographyPopulationMedicineSocial deprivationPopulation densityEmergency medical servicesService (business)Mortality rateGerontologyMedical emergencyEnvironmental healthBusinessSociology

Abstract

fetched live from OpenAlex

Demand for emergency ambulance services has risen steeply over the recent years. This study examined differences in work-load of ambulance services across England and investigated factors linked to high demand. The number of emergency calls received by each ambulance service in 1997 and 2002 and population and area data were used to calculate call rates and population density for each of 27 service areas. Deprivation score and proportion of the population under age 15 and over age 65 were calculated for each service area. There was wide variation in emergency call rates across England, with London having the highest rate both in 1997 (125.6 calls per 1000 persons) and in 2002 (140.1 per 1000). Statistically significant positive associations were observed between call rates and deprivation (1997, r = 0.49; 2002, r = 0.53) and between call rates and population density (1997, r = 0.70; 2002, r = 0.68). Following multivariable regression, the effect of deprivation score was consistently weaker, but the effect of population density was virtually unchanged. We conclude that areas with higher population density have higher call rates, which is not explained by deprivation. Deprivation is associated with higher usage, but its effect is partly due to population density. There is no evidence that these relationships are confounded by age.

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)0.004
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 score0.026
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.070
GPT teacher head0.409
Teacher spread0.339 · 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

Citations34
Published2006
Admission routes1
Has abstractyes

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