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Record W1972124413 · doi:10.12927/hcpol.2013.22752

The Use of Registered Nurses to Perform Flexible Sigmoidoscopy Procedures in Ontario: A Cost Minimization Analysis

2012· article· en· W1972124413 on OpenAlexaffvenueabout
Sarah Costa, Peter C. Coyte, Audrey Laporte, Laura Quigley, Shannon Elizabeth Reynolds

Bibliographic record

VenueHealthcare policy · 2012
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsInstitute for Work & HealthInstitute of Health Services and Policy ResearchUniversity of Toronto
Fundersnot available
KeywordsRemunerationSigmoidoscopyMedicineColorectal cancerCost-minimization analysisHealth careColorectal cancer screeningFamily medicineTest (biology)GynecologyCancerSurgeryInternal medicineBusinessPolitical scienceColonoscopy

Abstract

fetched live from OpenAlex

RATIONALE: Rates of colorectal cancer (CRC) are on the rise in Canada. Flexible sigmoidoscopy (FS) is an initial screening test for CRC primarily used in adults aged 50 years and older at average risk for the disease. Physicians and registered nurses have been shown to have the same effectiveness in performing a FS procedure. This paper presents an analysis of the use of registered nurses (RN) compared to physicians in Ontario to assess costs to the healthcare system. OBJECTIVES: To evaluate whether FS performed by RNs is a less costly alternative to increase access to CRC screening capacity in Ontario. METHODOLOGY: A cost minimization analysis was conducted from a health system perspective. DISCUSSION: RN-performed FS is a viable alternative for increasing CRC screening capacity in Ontario. Remuneration schedules for on-call physicians must be taken into consideration if policies are developed for the implementation of RN screening procedures. RESULTS: The findings suggest that the use of RNs may be cost saving compared to physician-performed FS procedures, depending on physician remuneration.

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)0.001
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 score0.219
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.137
GPT teacher head0.398
Teacher spread0.261 · 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
Published2012
Admission routes3
Has abstractyes

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