Exploring resident perceptions of initial competency based medical education implementation
Bibliographic record
Abstract
BACKGROUND: Competence by design (CBD) is a nationally developed hybrid competency based medical education (CBME) curricular model that focuses on residents' abilities to promote successful practice and better meet societal needs. CBD is based on a commonly used framework of five core components of CBME: outcome competencies, sequenced progression, tailored learning experiences, competency-focused instruction and programmatic assessment. There is limited literature concerning residents' perceptions of implementation of CBME. OBJECTIVE: We explored resident perceptions of this transformation and their views as they relate to the intended framework. METHODS: We recruited residents enrolled in current CBME implementation between August 2018 and January 2019. We interviewed residents representing eight disciplines from the initial two CBME implementation cohorts. Inductive thematic analysis was used to analyse the data through iterative consensus building until saturation. RESULTS: We identified five themes: 1) Value of feedback for residents; 2) Resident strategies for successful Entrustable Professional Activity observation completion; 3) Residents experience challenges; 4) Resident concerns regarding CBME; and 5) Resident recommendations to improve existing challenges. We found that while there was clear alignment with residents' perceptions of the programmatic assessment core CBME component, alignment was not as clear for other components. CONCLUSIONS: Residents perceived aspects of this transformation as helpful but overall had mixed perceptions and variable understanding of the intended underlying framework. Understanding and disseminating successes and challenges from the resident lens may assist programs at different stages of CBME implementation.
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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 | 0.022 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".