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Record W2051244219 · doi:10.1002/chp.1340240204

Severe acute respiratory syndrome and the delivery of continuing medical education: Case study from Toronto

2004· article· en· W2051244219 on OpenAlexaffabout
Dave Davis, David P. Ryan, Gary Sibbald, Anita Rachlis, Sharon Davies, L. Manchul, Sagar V. Parikh

Bibliographic record

VenueJournal of Continuing Education in the Health Professions · 2004
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsToronto Western HospitalMount Sinai HospitalSunnybrook HospitalUniversity of Toronto
Fundersnot available
KeywordsContinuing medical educationMedicineContinuing educationPublic healthSevere acute respiratory syndromeMedical emergencyMedical educationCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)NursingDisease

Abstract

fetched live from OpenAlex

INTRODUCTION: Severe acute respiratory syndrome (SARS) struck Toronto in the spring of 2003, causing many deaths, serious morbidity, forced quarantine of thousands of individuals, and the closure of all provincial hospitals for several weeks. Given the direction by public health authorities to cancel or postpone all continuing medical education (CME) courses, including those sponsored by the University of Toronto Faculty of Medicine, SARS has had a profound effect on the delivery of CME in Toronto and beyond. METHOD: Case study design using existing documents and self-report. RESULTS: The immediate, specific response of the University of Toronto CME program to SARS is described for the period from March 2003 to September 2003. DISCUSSION: During major outbreaks of infectious disease, continuing education providers should maintain regular contact with public health authorities and learners, enact a rational process for postponing or canceling courses, and implement a disaster plan flexible enough to ensure the deliver, of education using technological advances.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.814

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0060.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0030.002
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 head0.020
GPT teacher head0.411
Teacher spread0.391 · 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 designCase report
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

Citations20
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Continuing Education in the Health ProfessionsSame topicInnovations in Medical EducationFrench-language works237,207