Trusting our trainees: competency-based training of cardiologists using Entrustable Professional Activities (EPA)
Bibliographic record
Abstract
Abstract Assessment of trainees is a core activity of educators, ensuring that trainees can be trusted to provide high quality care with no supervision. Training and the demands on trainees have changed with specialization, need for more technical skills, digitization, needs for teamwork and greater communication skills. Competency-based medical education was introduced to capture these changing needs for trainees. The 7th ESC Education Conference – “From competence to good clinical care” – brought together national directors of training (39), young cardiology representatives (7), patients (7), ESC partners in education (10), and the ESC Education Committee (22) to discuss contemporary challenges in cardiology training and Entrustable Professional Activities (EPAs)-based solutions. Methods Pre- and post-conference surveys were conducted. The different issues were discussed in 4 workshops: core knowledge and evidence, skills and competence, performing into context, and training the trainers. Results Pre-conference, 90% of respondents believed that trainees should be certified only when they can be entrusted to perform in an unsupervised fashion, and 84% thought that specific training for educators should be required. From the workshops 4 themes emerged: 1) Core knowledge and evidence: rotations to different centres are needed to enable trainees to meet EPA requirements. 2) Skills and competence: a learning agreement between trainers and trainees should be established with protected time to achieve EPAs, and patient feedback on trainee performance should be obtained. 3) Performing into context (when is a trainee ready to practice): clinicians do informal assessment on a daily basis; EPAs formalize this with regular observation linked with progression in responsibility. 4) Train the trainer: good doctors are not automatically good trainers and should be required to undergo specific training themselves. We reviewed complex training frameworks such as the Canadian Medical Education Directives for Specialists (CanMEDS) that contains too many components to be useful for clinical teachers. EPAs were identified as a practical way to implement competency-based cardiology training across the ESC. The 2020 ESC Core Curriculum contains EPAs that a cardiologist should be able to perform independently by the end of training. Post-conference there was unanimous agreement (100%) that EPAs are a valuable concept in training. A majority of participants (69%) agreed that EPA's are applicable in practice today. Conclusion Trainee assessment is a daily challenge for educators. The ESC Education Conference identified distinct goals to be achieved before, during and at the completion of a training programs. The EPA concept was widely accepted as an efficient method to monitor trainees progress, while trusting them to perform activities in which they are proficient. Trusting our trainees should be a major educational goals for cardiologists in Europe. Funding Acknowledgement Type of funding source: None
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".