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Record W3215163152 · doi:10.36834/cmej.73008

Medical student wellness in Canada: time for a national curriculum framework

2021· article· en· W3215163152 on OpenAlexaffvenueabout
Dax Bourcier, Rena Far, Lucas B King, George Cai, J A Mader, Maggie Z. X. Xiao, Christopher Simon, Taylor McFadden, Leslie Flynn

Bibliographic record

VenueCanadian Medical Education Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsQueen's UniversityCanadian Medical AssociationMemorial University of NewfoundlandUniversity of CalgaryMcGill UniversityUniversity of AlbertaUniversity of SaskatchewanDalhousie University
Fundersnot available
KeywordsAccreditationCurriculumAdaptabilityMedical educationPsychologyMedicinePedagogyManagement

Abstract

fetched live from OpenAlex

There is substantial evidence showing that medical student wellness is a worsening problem in Canada. It is apparent that medical students' wellness deteriorates throughout their training. Medical schools and their governing bodies are responding by integrating wellness into competency frameworks and accreditation standards through a combination of system- and individual-level approaches. System-level strategies that consider how policies, medical culture, and the "hidden curriculum" impact student wellness, are essential for reducing burnout prevalence and achieving optimal wellness outcomes. Individual-level initiatives such as wellness programming are widespread and more commonly used. These are often didactic, placing the onus on the student without addressing the learning environment. Despite significant progress, there is little programming consistency across schools or training levels. There is no wellness curriculum framework for Canadian undergraduate medical education that aligns with residency competencies. Creating such a framework would help align individual- and system-level initiatives and smooth the transition from medical school to residency. The framework would organize goals within relevant wellness domains, allow for local adaptability, consider basic learner needs, and be learner-informed. Physicians whose wellness has been supported throughout their training will positively contribute to the quality of patient care, work environments, and in sustaining a healthy Canadian population.

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)0.002
metaresearch head score (Gemma)0.094
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.611
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.094
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 science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.1020.000

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 head0.007
GPT teacher head0.340
Teacher spread0.332 · 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

Citations15
Published2021
Admission routes3
Has abstractyes

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