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Record W2532130708 · doi:10.1111/coa.12772

Otolaryngology residency education: a scoping review on the shift towards competency‐based medical education

2016· review· en· W2532130708 on OpenAlexaff
Natalie Wagner, Christine Fahim, Kate M. Dunn, D. Reid, Ranil Sonnadara

Bibliographic record

VenueClinical Otolaryngology · 2016
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineCINAHLFormative assessmentMedical educationMEDLINECurriculumSystematic reviewOtorhinolaryngologyNursingSurgeryPsychological interventionPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Residency training programmes worldwide are experiencing a shift from the traditional time-based curriculum to competency-based medical education (CBME), due to changes in the healthcare system that have impacted clinical learning opportunities. Otolaryngology-Head and Neck Surgery (OTL-HNS) programmes are one of the first North American surgical specialties to adopt the new CBME curriculum. OBJECTIVE OF REVIEW: The purpose of this scoping review is to examine the literature pertaining to CBME in OTL-HNS programmes worldwide, to identify the tools that have been developed and identify potential barriers to the implementation of CBME. SEARCH STRATEGY: Four online databases, OVID MEDLINE (R) from 1946 to 5 August 2015, EMBASE 1974 to 5 August 2015, Cochrane and CINAHL databases up to 5 August 2015, were searched using key words related to OTL-HNS and CBME. EVALUATION METHOD: Two researchers independently reviewed the literature in a systematic manner and met to discuss and address any discrepancies at each step of the review process. RESULTS: Of the 207 publications identified in the initial search, 31 were included in this scoping review. Two key themes emerged from the literature: first, OTL-HNS programmes reported a need for new assessment tools that assess competency and also provide the learner with formative feedback. Second, although varieties of tools assessing both technical and non-technical skills have been developed, implementation of such tools has been met with some challenges. These challenges include a lack of faculty support, inadequate administrative support and a lack of knowledge on how to start the transition to CBME. CONCLUSIONS: This scoping review suggests that task-specific checklists, entrustment scales, evaluation portfolios from multiple assessments and faculty training sessions are key aspects to incorporate as OTL-HNS training programmes shift towards a CBME curriculum.

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)0.018
metaresearch head score (Gemma)0.069
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.069
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0220.023
Science and technology studies0.0020.002
Scholarly communication0.0040.005
Open science0.0030.003
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.091
GPT teacher head0.490
Teacher spread0.399 · 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.

Study designSystematic review
DomainMethods
GenreReview

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

Citations50
Published2016
Admission routes1
Has abstractyes

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