Otolaryngology residency education: a scoping review on the shift towards competency‐based medical education
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".