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Record W2111341750 · doi:10.1186/1472-6920-13-3

Global health education in U.S. Medical schools

2013· article· en· W2111341750 on OpenAlexaff
Omar A. Khan, Richard L. Guerrant, James H. Sanders, Charles C. J. Carpenter, Margaret Spottswood, David S. Jones, Cliff O’Callahan, Timothy F. Brewer, Jeffrey F. Markuns, Stephen Gillam, Joseph F. O’Neill, Neal Nathanson, Stephen Wright

Bibliographic record

VenueBMC Medical Education · 2013
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedical educationCurriculumMEDLINEMedicinePolitical sciencePsychologyPedagogy

Abstract

fetched live from OpenAlex

Interest in global health (GH) among medical students worldwide is measurably increasing. There is a concomitant emphasis on emphasizing globally-relevant health professions education. Through a structured literature review, expert consensus recommendations, and contact with relevant professional organizations, we review the existing state of GH education in US medical schools for which data were available. Several recommendations from professional societies have been developed, along with a renewed emphasis on competencies in global health. The implementation of these recommendations was not observed as being uniform across medical schools, with variation noted in the presence of global health curricula. Recommendations for including GH in medical education are suggested, as well as ways to formalize GH curricula, while providing flexibility for innovation and adaptation.

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)0.014
metaresearch head score (Gemma)0.030
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.008
Science and technology studies0.0020.002
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.015
GPT teacher head0.404
Teacher spread0.389 · 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

Citations118
Published2013
Admission routes1
Has abstractyes

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