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Record W3186954780 · doi:10.9778/cmajo.20200236

Interventions to improve the well-being of medical learners in Canada: a scoping review

2021· review· en· W3186954780 on OpenAlexvenueaboutno aff
Stephana J. Moss, Krista Wollny, Mungunzul Amarbayan, Diane Lorenzetti, Aliya Kassam

Bibliographic record

VenueCMAJ Open · 2021
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsCINAHLPsychological interventionPsycINFOMEDLINEMedicineMedical educationIntervention (counseling)Graduate medical educationFamily medicinePsychologyNursingAccreditation

Abstract

fetched live from OpenAlex

2] Social, mental, and physical wellbeing have been shown to be negatively affected during undergraduate medical education, 7 with increased prevalence of burnout in residency. Poor well-being can affect medical learners across the spectrum of programs, including undergraduate health sciences. he Canadian Federation of Medical Students aims to "train healthier physicians to maximize the productivity and quality of health care services for Canadians." 11 Their strategic directions for 2020-2022 include developing health promoting communities, promoting a positive culture in medical education that prioritizes learner well-being, increasing collaboration within the medical community and optimizing student resources. Despite the growing literature as universities implement services to address the well-being of medical learners, earlier reviews on this topic have not evaluated interventions in Canadian medical schools and have found it difficult to define medical learner well-being. he Wellness Innovation Scholarship for Health Professions Education and Health Sciences (WISHES) laboratory at the University of Calgary is taking a holistic approach to medical learner well-being. 14 Based on Nussbaum's human capabilities approach 15 and acknowledging that well-being is multi dimensional, 16 WISHES focuses on measurable outcomes within the domains of mental, physical, occupational,

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)0.003
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.756
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.085
GPT teacher head0.477
Teacher spread0.392 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations13
Published2021
Admission routes2
Has abstractyes

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