Development of an integrated competency framework for postgraduate paediatric training: a Delphi study
Bibliographic record
Abstract
Competency-based education (CBE) has transformed medical training during the last decades. In Flanders (Belgium), multiple competency frameworks are being used concurrently guiding paediatric postgraduate CBE. This study aimed to merge these frameworks into an integrated competency framework for postgraduate paediatric training. In a first phase, these frameworks were scrutinized and merged into one using the Canadian Medical Education Directives for Specialists (CanMEDS) framework as a comprehensive basis. Thereafter, the resulting unified competency framework was validated using a Delphi study with three consecutive rounds. All competencies (n = 95) were scored as relevant in the first round, and twelve competencies were adjusted in the second round. After the third round, all competencies were validated for inclusion. Nevertheless, differences in the setting in which a paediatrician may work make it difficult to apply a general framework, as not all competencies are equally relevant, applicable, or suitable for evaluation in every clinical setting. These challenges call for a clear description of the competencies to guide curriculum planning, and to provide a fitting workplace context and learning opportunities.Conclusion: A competency framework for paediatric post-graduate training was developed by combining three existing frameworks, and was validated through a Delphi study. This competency framework can be used in setting the goals for workplace learning during paediatric training. What is Known: •Benefits of competency-based education and its underlying competency frameworks have been described in the literature. •A single and comprehensive competency framework can facilitate training, assessment, and certification. What is New: •Three existing frameworks were merged into one integrated framework for paediatric postgraduate education, which was then adjusted and approved by an expert panel. •Differences in the working environment might explain how relevant a competency is perceived.
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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 | 0.114 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".