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Record W2414275937

Rural longitudinal integrated clerkships: changing interests and demographics of medical students.

2015· article· en· W2414275937 on OpenAlexaffabout
Douglas Myhre, Paul Adamiak, Wayne Woloschuk, Tyrone Donnon

Bibliographic record

VenuePubMed · 2015
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDemographicsCurriculumMedicineMedical schoolMedical educationRural areaLongitudinal studyFamily medicineGerontologyDemographyPsychologyPedagogySociology
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: The University of Calgary Longitudinal Integrated Clerkship (UCLIC) is an integrated curriculum of at least 32 weeks' duration based in rural communities. Rural LICs have been proposed as a method to respond to the needs of underserved rural communities; therefore, assessing evolving learner interest and demographics over time is of importance to rural communities. METHODS: Three surveys were administered to first-year medical students at the University of Calgary from the classes of 2009, 2010 and 2015. The surveys assessed demographic information as well as interest in and attitudes toward pursuing a rural-based LIC. RESULTS: Overall, 42% of students (76% of decided students) reported that they would consider the rural UCLIC. Between 2009 and 2010, the proportion of students who would not consider the UCLIC decreased from 25% to 8%, and thereafter was maintained at that level. Over the same period, interest among students considering Royal College of Physicians and Surgeons of Canada (RCPSC) specialties significantly increased. Although student attitudes about the value of the LIC were consistently positive, students remained concerned about social considerations. CONCLUSION: There has been an increase in student willingness to consider a rural LIC, most significantly among students interested in RCPSC specialties. Career plans and demographics of students continue to influence their interest in and attitudes toward LICs.

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)0.001
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 score0.023
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 science0.0010.001
Research integrity0.0000.001
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 head0.131
GPT teacher head0.444
Teacher spread0.313 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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