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Record W3010856333 · doi:10.1136/fmch-2019-000250

Global health training in Canadian family medicine residency programmes

2020· article· en· W3010856333 on OpenAlexaffabout
Divyanshi Jalan, Helene Morakis, Neil Arya, Yassen Tcholakov, Jennifer Carpenter, William Cherniak

Bibliographic record

VenueFamily Medicine and Community Health · 2020
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsQueen's UniversityMcGill UniversityUniversity of British ColumbiaUniversity of TorontoMcMaster University
Fundersnot available
KeywordsWorkforceScope (computer science)Family medicineMedical educationResidency trainingMedicineScope of practicePolitical scienceContinuing educationHealth care

Abstract

fetched live from OpenAlex

Objective: Canadian family medicine (FM) residency programmes are responding to the growing demand to provide global health (GH) education to their trainees; herein, we describe the various GH activities (GHAs) offered within Canadian FM programmes. Design: A bilingual online survey was sent out to all 17 Canadian FM program directors (PDs) and/or an appointed GH representative. Setting: Online survey via Qualtrics. Participants: All 17 Canadian FM PDs and/or an appointed GH representative. Results: The response rate was 100% and represented 3250 first-year and second-year FM residents across English and French Canada. All schools stated that they participate in some form of GHAs. There was variation in the level of organisation, participation and types of GHAs offered. Overall, most GHAs are optional, and there is a large amount of variation in terms of resident participation. Approximately one third of programmes receive dedicated funding for their GHAs, and two thirds wish to increase the scope/variety of GHAs. Conclusion: These results suggest nationwide interest in developing a workforce trained in GH, but show great discrepancies in training, implementation and education.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.388
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.157
GPT teacher head0.412
Teacher spread0.255 · 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 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
Published2020
Admission routes2
Has abstractyes

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