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Record W4213254016 · doi:10.7759/cureus.22344

Competency-Based Medical Education: Are Canadian Pediatric Anesthesiologists Ready?

2022· article· en· W4213254016 on OpenAlexaffabout
Katherine Bailey, Nicholas West, Clyde Matava

Bibliographic record

VenueCureus · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoBritish Columbia Children's Hospital
Fundersnot available
KeywordsMedicineCurriculumLikert scaleMedical educationQuarter (Canadian coin)House staffFamily medicinePsychologyPedagogy

Abstract

fetched live from OpenAlex

Background With the introduction of Competency-Based Medical Education (CBME), the Canadian Pediatric Anesthesia Society (CPAS) surveyed its members to assess their awareness of and prior experience with CBME concepts and evaluation tools, and identify methods for faculty development of CBME teaching strategies for pediatric anesthesia residents and fellows. Methods An online survey was sent to CPAS members. Outcomes included respondents' previous exposure to CBME and the educational support they had received in anticipation of the curriculum. Questions used multi-item Likert scales and a general feedback question. Results The response rate was 39% (60/155). Eighty-eight percent of respondents spent ≥50% of their time practicing pediatric anesthesia; 78% and 45% spent at least a quarter of their time teaching residents and fellows respectively. Eighty-three percent were familiar with CBME concepts, and 58% were familiar with Milestones, Competencies, and Entrustable Professional Activities (EPAs). However, 64% had not received any formal training and 52% had not used any CBME evaluation tools. Learning preferences included small group discussions (72%), lectures with questions and answers (Q&A) (62%), seminars (50%), and workshops (50%). Conclusions Despite widespread awareness of CBME concepts, there is a need to educate Canadian pediatric anesthesiologists regarding CBME evaluation tools. Faculty development support will increase the utilization of these tools in teaching practice.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.015
GPT teacher head0.275
Teacher spread0.259 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

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