Competence by Design in Cardiac Surgery Resident Training: A Qualitative Thematic Analysis
Bibliographic record
Abstract
INTRODUCTION: A new training model known as "Competence by Design" (CBD) is centered on evaluating "entrustable professional activities" and "milestones" and it represents a paradigm change from time-based to outcome-based learning and evaluation. This study presents a qualitative quality assurance and improvement assessment of the current state of CBD in cardiac surgery training at a single center. METHODS: An initial questionnaire was distributed to three focus groups: educators, traditional-system trainees, and CBD trainees. Building on the questionnaire responses, in-depth interviews were conducted and qualitative thematic data analysis was performed to identify recurrent themes. RESULTS: Thirteen participants were interviewed (6 educators and 7 residents, n = 4 traditional-system trainees and n = 3 CBD trainees). Thematic analysis generated 16 themes, including six major themes. CBD (1) promotes a more standardized approach to surgical training, (2) allows for more objective assessment of residents' progress, (3) encourages a focused approach to specific skill development, (4) comes with increased administrative workloads, (5) allows for early recognition of struggling or failing residents with documentation, and (6) presents challenges in understanding and implementation for both residents and educators. CONCLUSIONS: To our knowledge, this is the first study to assess the benefits and pitfalls of CBD in a Canadian cardiac surgery training program with feedback from both educators and trainees. Our participants felt that CBD has value in providing more standardized training, more elaborate and well-documented assessments, more detailed and meaningful feedback, and outcome-based training focused on the acquisition of surgical skills despite increased administrative workloads. Our participants identified specific challenges involved in understanding and implementing the CBD model.
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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 | 0.040 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| 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".