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Record W4415298033 · doi:10.2147/amep.s542705

Advancing Rural Generalist Training: The Northern Regional Integrated Clerkship, a Blended Longitudinal Integrated Clerkship Innovation in Northern British Columbia

2025· article· en· W4415298033 on OpenAlexaffabout
Sean B Maurice, Maggie Watt, Andrea Gingerich, Paul Winwood

Bibliographic record

VenueAdvances in Medical Education and Practice · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Northern British ColumbiaUniversity of British Columbia
Fundersnot available
KeywordsAccreditationIndigenousCurriculumRural areaCircumpolar starMandateEquity (law)Rural healthGeneralist and specialist speciesAccountability

Abstract

fetched live from OpenAlex

The Northern Medical Program (NMP) is the most rural site of the University of British Columbia's distributed medical school. Situated in northern British Columbia, Canada, the NMP strives for health equity by training physicians to meet the needs of northern, rural and Indigenous patients with innovative approaches to curriculum delivery which maintain comparability to the rest of the program and alignment with the assessment structure. Medical schools have a social accountability mandate to train physicians to serve the needs of rural and Indigenous peoples, to address the challenges of rural recruitment and retention, and to eliminate health inequities. Here we describe the creation of a new blended clerkship which places students in a small rural community for a 6-month longitudinal integrated clerkship and in a small urban community for a 6-month rotational clerkship. This new clerkship provides a unique opportunity for learners to learn and experience rural generalist medicine while meeting the accreditation standard of comparability. Early implementation has shown promise in enhancing rural medical education while maintaining curricular comparability, with positive reception from students and faculty.

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)0.004
metaresearch head score (Gemma)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.042
GPT teacher head0.451
Teacher spread0.410 · 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 designNot applicable
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

Citations0
Published2025
Admission routes2
Has abstractyes

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