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Record W2291559651

Practice locations of longitudinal integrated clerkship graduates: a matched-cohort study.

2016· article· en· W2291559651 on OpenAlexaffabout
Douglas Myhre, Sameer Bajaj, Wayne Woloschuk

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEconomic shortageRural areaMedical educationCohortLicensureMedicineLibrary scienceHumanitiesInternal medicineComputer science
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: Longitudinal integrated clerkships (LICs) have been introduced as an innovative model to impart medical education. In Canada, most LIC experiences are situated in rural communities. Studies have reported equivalence in graduates from rural LICs and traditional rotation-based clerkships (RBCs) in their performance in residency, as well as in national medical licensure examinations. We sought to determine the impact of rural LICs in terms of practice location of graduates. METHODS: A matched cohort was developed on the basis of student background and sex to compare practice location of rural LIC and RBC graduates. We used the χ(2) test to assess the association between type of clerkship stream and practice location. RESULTS: We found an association between participation in a rural LIC and rural practice location. CONCLUSION: Rural LIC programs play an important role in introducing students to rural medicine and may be an effective tool in responding to the shortage of rural practitioners.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.093
GPT teacher head0.415
Teacher spread0.322 · 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 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

Citations23
Published2016
Admission routes2
Has abstractyes

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