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Record W2049860773 · doi:10.1089/153056204773644580

An Audit of the Appropriateness of Teletriage Nursing Advice

2004· article· en· W2049860773 on OpenAlexafffundabout
John C. Hogenbirk, Raymond Pong

Bibliographic record

VenueTelemedicine Journal and e-Health · 2004
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsLaurentian University
FundersIvey Foundation
KeywordsAuditAdvice (programming)Generalizability theoryNursingMedicineFamily medicinePsychologyBusinessAccounting

Abstract

fetched live from OpenAlex

This study assessed the appropriateness of advice given by teletriage nurses to patients in northern Ontario. Assessments used audiotapes and printed records of 73 calls, selected from approximately 350 calls based on sound quality, completeness, and consent of caller and teletriage nurse. Audits were conducted independently by one family physician, one nurse practitioner, and one registered nurse with teletriage experience. In 56% of the 73 calls, all three auditors judged the nurse's advice as "appropriate." In 92% of the 73 calls, at least two of the three auditors judged the teletriage nurse's advice as "appropriate." All calls were rated as "appropriate" by at least one auditor. If not "appropriate," then auditors were three times more likely to rate the advice as "overly-cautious" rather than "insufficient." The percentage of calls with the same rating varied from 62% to 86% with an outlier of 33%. Nurse practitioners tended to rate the appropriateness of the advice slightly, but significantly lower than the rating given by family physicians or registered nurses. Interestingly, nurse practitioners tended to rate aspects of the nurse-caller interaction advice as slightly and significantly better than the rating chosen by family physicians or registered nurses. The teletriage service was providing appropriate advice, but the generalizability of these results may be limited because of the selection of calls.

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)0.003
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 score0.131
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.079
GPT teacher head0.455
Teacher spread0.376 · 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

Citations11
Published2004
Admission routes3
Has abstractyes

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