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Record W2981879141 · doi:10.1177/0840470419882954

A rural approach to quality improvement for small rural hospitals: Lessons from rural Texas

2019· article· en· W2981879141 on OpenAlexaffabout
Robert S. Steele, Elizabeth Wenghofer, Tammy Wagner, Peter Yu, Nancy W. Dickey

Bibliographic record

VenueHealthcare Management Forum · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsLaurentian University
Fundersnot available
KeywordsContext (archaeology)Rural areaRural healthHealth careQuality (philosophy)NursingQuality managementMedicineFamily medicineMedical educationBusinessPolitical scienceGeographyMarketing

Abstract

fetched live from OpenAlex

This article describes the Rural Physician Peer Review Program (RPPR©) developed by the Texas A&M Rural and Community Health Institute and presents it as an example of a program that could be implemented in rural Canada as an effective means of continuing professional development (CPD) for rural Canadian physicians. RPPR© post review survey responses from 574 physician participants across rural Texas indicate that they are highly satisfied with RPPR© and that their competency in medical knowledge and patient care improves as a result of participation. A pilot project with two to four northern Ontario hospitals would enable RPPR© to be modified to ensure applicability and feasibility in the northern Ontario context to create an RPPR© "North." New and innovative approaches to CPD for rural northern physicians need to be continually explored to decrease professional isolation, improve recruitment and retention, and ultimately improve the quality and safety of healthcare in rural areas.

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)0.009
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.003
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.034
GPT teacher head0.368
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 designQualitative
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

Citations3
Published2019
Admission routes2
Has abstractyes

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