Development of EPAT: An assessment tool for pediatric hematology/oncology training programs
Bibliographic record
Abstract
PURPOSE: In the absence of a standardized tool to assess the quality of pediatric hematology/oncology training programs, the Education Program Assessment Tool (EPAT) was conceptualized as a user-friendly and adaptable tool to evaluate and identify areas of opportunity, pinpoint needed modifications, and monitor progress for training programs around the world. METHODS: The development of EPAT consisted of three main phases: operationalization, consensus, and piloting. After each phase, the tool was iteratively modified based on feedback to improve its relevance, usability, and clarity. RESULTS: The operationalization process led to the development of 10 domains with associated assessment questions. The two-step consensus phase included an internal consensus phase to validate the domains and a subsequent external consensus phase to refine the domains and overall function of the tool. EPAT domains for programmatic evaluation are hospital infrastructure, patient care, education infrastructure, program basics, clinical exposure, theory, research, evaluation, educational culture, and graduate impact. EPAT was piloted in five training programs in five countries, representing diverse medical training and patient care contexts for proper validation of the tool. Face validity was confirmed by a correlation between the perceived and calculated scores for each domain (r = 0.78, p < .0001). CONCLUSIONS: EPAT was developed following a systematic approach, ultimately leading to a relevant tool to evaluate the different core elements of pediatric hematology/oncology training programs across the world. With EPAT, programs will have a tool to quantitatively evaluate their training, allowing for benchmarking with centers at the local, regional, and international level.
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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 | 0.037 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".