Cardiac Critical Care Fellowship Training in the United States and Canada: Pediatric Cardiac Intensive Care Society-Endorsed Subcompetencies to the 2022 Entrustable Professional Activities*
Bibliographic record
Abstract
OBJECTIVES: We aimed to define and map subcompetencies required for pediatric cardiac critical care (PCCC) fellowship education and training under the auspices of the Pediatric Cardiac Intensive Care Society (PCICS). We used the 2022 frameworks for PCCC fellowship learning objectives by Tabbutt et al and for entrustable professional activities (EPAs) by Werho et al and integrated new subcompetencies to the EPAs. This complementary update serves to provide a foundation for standardized trainee assessment tools for PCCC. DESIGN: A volunteer panel of ten PCICS members who are fellowship education program directors in cardiac critical care used a modified Delphi method to develop the update and additions to the EPA-based curriculum. In this process, the experts rated information independently, and repetitively after feedback, before reaching consensus. The agreed new EPAs were later reviewed and unanimously accepted by all PCICS program directors in PCCC in the United States and Canada and were endorsed by the PCICS in 2023. PROCEDURE AND MAIN RESULTS: The procedure for defining new subcompetencies to the established EPAs comprised six consecutive steps: 1) literature search; 2) selection of key subcompetencies and curricular components; 3) written questionnaire; 4) consensus meeting and critical evaluation; 5) approval by curriculum developers; and 6) PCICS presentation and endorsement. Overall, 110 subcompetencies from six core-competency domains were mapped to nine EPAs with defined levels of entrustment and examples of simple and complex cases. To facilitate clarity and develop a future assessment tool, three EPAs were subcategorized with subcompetencies mapped to the appropriate subcategory. The latter covering common procedures in the cardiac ICU. CONCLUSIONS: This represents the 2023 update to the PCCC fellowship education and training EPAs with the defining and mapping of 110 subcompetencies to the nine established 2022 EPAs. This goal of this update is to serve as the next step in the integration of EPAs into a standardized competency-based assessment framework for trainees in PCCC.
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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 | 0.036 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| 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".