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Record W2921090841 · doi:10.5206/uwomj.v84i2.4272

Multimorbidity in family medicine clerkship

2016· article· en· W2921090841 on OpenAlexvenueaboutno aff
Emily Harrison

Bibliographic record

VenueUniversity of Western Ontario Medical Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCurriculumFamily medicineInclusion (mineral)PopulationClinical clerkshipMedical schoolMedical educationPsychologyPedagogy

Abstract

fetched live from OpenAlex

PURPOSE: To document senior medical students’ experiences in caring for patients with multiple chronic illnesses in family medicine clerkship and explore their attitudes towards the inclusion of this topic in existing curricula. METHODS: A cohort of third-year medical students from the Schulich School of Medicine and Dentistry at the University of Western Ontario were surveyed following their core family medicine clerkship. RESULTS: One hundred percent of students surveyed participated in the care of patients with multiple chronic illnesses during their family medicine clerkship. However, only 28% percent reported receiving formal teaching on this topic while 89.5% felt that multimorbidity should be taught at the clerkship level. The majority of students surveyed felt comfortable caring for this patient population. CONCLUSION: Patients with multiple chronic illnesses are common in family practice. All third-year medical students encountered patients with multimorbidity during their family medicine clerkship. This study contributes to a growing body of literature that suggests the need for a shift in medical education and health care delivery in order to better serve an increasingly complex patient 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 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)0.002
metaresearch head score (Gemma)0.007
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.069
GPT teacher head0.298
Teacher spread0.229 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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