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Record W3045410697 · doi:10.36834/cmej.70457

Impact of the COVID-19 pandemic on anesthesia residency education

2020· article· en· W3045410697 on OpenAlexafffundvenueabout
Jennifer O’Brien, Megan Deck, Una Goncin, Malone Chaya

Bibliographic record

VenueCanadian Medical Education Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Context (archaeology)MedicineResidency trainingAirway management2019-20 coronavirus outbreakMedical educationWelfareSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PerceptionNursingMedical emergencyAnesthesiaAirwayPsychologyPolitical scienceContinuing educationVirology

Abstract

fetched live from OpenAlex

The clinical role of anesthesia residents during the COVID-19 pandemic has not been well described. As qualified physicians trained in airway management, anesthesia residents could be considered essential personnel. Given the uncertain supply of protective equipment, decision-makers must consider the welfare of trainees in any decision to deploy anesthesia residents. This national survey of Canadian anesthesia residents will develop our understanding of medical education, safety, and perceptions towards training in the context of the COVID-19 pandemic. Our results may inform the Royal College of Physicians and Surgeons, program directors, and health officials in optimizing anesthesia residency training during future pandemic conditions.

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)0.001
metaresearch head score (Gemma)0.045
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.504
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.082
GPT teacher head0.439
Teacher spread0.357 · 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

Citations5
Published2020
Admission routes4
Has abstractyes

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