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Record W2205110204 · doi:10.4103/1357-6283.120693

Global child health education in Canadian paediatric residency programs

2013· article· en· W2205110204 on OpenAlexaffabout
TobeyAnn Audcent, Heather MacDonnell, Lindy Samson, JenniferL Brenner

Bibliographic record

VenueEducation for Health · 2013
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of CalgaryAlberta Children's HospitalChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsMedical educationChild healthFamily medicinePsychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

CONTEXT: Globalisation has led to significant changes in health care, yet medical education remains domestically focused. The majority of the world's children live in developing countries, and education related to global child health is important for paediatric residents. METHODS: Chief residents and program directors from the 16 Canadian paediatric training programs were surveyed using a questionnaire regarding global child health training program content, electives, attitudes and perceptions towards global child health. RESULTS: No programs had a formalised global health curriculum. All program directors and chief residents reported that programs offer global child health sessions, but 50% of the programs did not address six out of twelve of the content areas including topics such as refugee health and international adoption. All program directors agreed global child health understanding is important for paediatric trainees; 83% agreed more emphasis should be placed on this during post-graduate training. DISCUSSION: A formalised global child health curriculum is lacking for Canadian paediatric residents: Program directors are willing to integrate global child health training modules into their post-graduate training programs.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.264
Threshold uncertainty score0.531

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.013
GPT teacher head0.357
Teacher spread0.344 · 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

Citations5
Published2013
Admission routes2
Has abstractyes

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