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Record W2729066084 · doi:10.1093/geroni/igx004.202

LESSONS LEARNED FROM RENFREW COUNTY’S COMMUNITY PARAMEDIC RESPONSE UNIT PROGRAM

2017· article· en· W2729066084 on OpenAlexaff
M. Nolan, Samir K. Sinha

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsSinai Health SystemUniversity of Toronto
Fundersnot available
KeywordsMedicineUnit (ring theory)Community hospitalCohortEmergency responseMedical emergencyEmergency medicineNursingPsychology

Abstract

fetched live from OpenAlex

The County of Renfrew Community Paramedic Response Unit (CPRU) program was designed to improve emergency response times, promote prevention activities and support older adults living in the community. Community Paramedics focus on preventative measures to support clients live independently. In 2015, 222 patients were enrolled in CPRU and received a total of 1186 home visits and 2874 assessments from community paramedics. An evaluation of the CPRU demonstrated following the first year of implementation demonstrated that all enrolled patients remained independent and were still living in their own home. Further, clients had a 35% decrease in hospital admissions one-year after being enrolled in the program. This same cohort experienced an 83% reduction in total hospital readmission rates 30, 60 and 90 days after receiving a visit from the paramedic.

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)0.003
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 score0.331
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.187
GPT teacher head0.440
Teacher spread0.253 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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