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Record W2090968827 · doi:10.1207/s15328015tlm1704_11

A Rural and Regional Community Multi-Specialty Residency Training Network Developed by the University of Western Ontario

2005· article· en· W2090968827 on OpenAlexaffabout
James Rourke

Bibliographic record

VenueTeaching and Learning in Medicine · 2005
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsWestern University
Fundersnot available
KeywordsSpecialtyResidency trainingTraining (meteorology)Medical educationRural communityMedicineFamily medicineGerontologyGeographySociologyDemographyContinuing education

Abstract

fetched live from OpenAlex

BACKGROUND: Traditionally, specialty vocational/residency training is totally done in tertiary care university-hospital settings, making it very difficult for specialty residents to learn about the joys and challenges of rural and regional patient care. DESCRIPTION: The University of Western Ontario's Multi-Specialty Community Training Network (MSCTN) was developed to provide specialty residents with the opportunity to do part of their postgraduate vocational training in rural and regional practice settings. The network involves 10 medical school departments/divisions and 7 rural/regional communities. From 1997 to 2004, 174 residents have completed 287 months of rural/regional training. EVALUATION: Residents rated their overall learning experience at 6.41 on a 7-point scale. Nineteen of the 39 graduating residents have chosen to practice in rural and regional underserviced communities. CONCLUSION: Rural/regional specialty postgraduate vocational training rotations can provide excellent learning experiences. Preliminary results indicate that this exposure encourages many specialty residents to establish rural and regional practices.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.005
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 head0.110
GPT teacher head0.401
Teacher spread0.291 · 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.

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

Citations16
Published2005
Admission routes2
Has abstractyes

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