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Record W3197017548 · doi:10.1503/cjs.011520

Competency-based education in general surgery: Are Canadian residents ready?

2021· article· en· W3197017548 on OpenAlexafffundvenueabout
Gabrielle Gauvin, Kathryn Hay, Wilma M. Hopman, Scott Hurton, Stephanie Lim, Boris Zevin, Diederick Jalink, Sulaiman Nanji

Bibliographic record

VenueCanadian Journal of Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsCentre intégré de santé et de services sociaux de Chaudière-AppalachesUniversity of ManitobaCentre Intégré de Santé et de Services Sociaux des LaurentidesCégep de l'OutaouaisQueen's University
FundersQueen's University
KeywordsMedicineMEDLINEMedical educationFamily medicineGeneral surgery

Abstract

fetched live from OpenAlex

Summary: Competency-based education (CBE) is currently being implemented by the Royal College of Physicians and Surgeons of Canada across all residency programs. This shift away from time-based residency is proposed to be the answer to maximize training opportunity in the era of work hour restrictions and growing concerns regarding accountability in medical education. A Web-based survey was conducted to obtain feedback from Canadian general surgery residents on their experience and perception of competence within core procedures, as well as attitudes toward CBE. A total of 244 residents completed the survey. For most procedures, more than 50% of residents felt they could perform the procedure with no guidance after completing 11-30 cases. Generally, residents were welcoming of CBE; however, medium-sized programs reported some concerns regarding inadequate exposure to cases and risk of training less well-rounded surgeons. This is valuable resident feedback for programs to consider during the implementation process.

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)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.482
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.297
Teacher spread0.263 · 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.

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

Citations9
Published2021
Admission routes4
Has abstractyes

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