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Record W7132875209

Evaluation of the demonstration project to direct low acuity ambulance patients to urgent care centres to improve ambulance availability

2008· dissertation· W7132875209 on OpenAlexaboutno aff
Ali Vahit Esensoy

Bibliographic record

VenueTSpace · 2008
Typedissertation
Language
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTriageAmbulance serviceEmergency medical servicesFocus groupPsychosocialGovernment (linguistics)Phone
DOInot available

Abstract

fetched live from OpenAlex

The Ontario Government initiated a demonstration project in Toronto where select patients with minor medical conditions travelling via ambulance will be transported to Urgent Care Centres (UCC) instead of emergency departments (ED) to improve ambulance availability in the city. This thesis presents the evaluation of this project to determine the feasibility of this strategy in improving ambulance availability. The quantitative aspect of the study utilizes a dataset generated via the probabilistic linkage of ambulance and ED patient records, while focus groups and interviews support the qualitative side. It was found that the demonstration project reached 50% of potential ambulance volumes and reclaimed 855 hours of ambulance time over two years. Twenty-four hour operation of the sites and acceptance patients with psychosocial problems stand out as changes that can significantly improve ambulance availability benefits of the UCCs. Implementation of paramedic field triage protocols is the primary challenge of the initiative.

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)0.014
metaresearch head score (Gemma)0.018
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.362
Teacher spread0.334 · 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
Published2008
Admission routes1
Has abstractyes

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