Development and validation of a pediatric transfusion medicine education assessment tool
Bibliographic record
Abstract
BACKGROUND: Pediatric transfusion medicine presents unique challenges due to differences in pediatric physiology and pathology compared to adults. Research shows that pediatric healthcare professionals have knowledge gaps in transfusion medicine, contributing to suboptimal practice. To address these gaps, Transfusion Camp, a transfusion medicine education program, was adapted to a pediatric-focused curriculum. To assess pediatric learners' knowledge uptake, we aimed to develop and validate the Pediatric Transfusion Knowledge Test. STUDY DESIGN AND METHODS: The BEST TEST-3, a previously validated transfusion medicine knowledge assessment tool, was modified by a multidisciplinary committee of content experts, using a curriculum blueprint for pediatric transfusion medicine consisting of 31 core and 42 extended topics, identified through a previously published national multi-specialty Delphi consensus study. The modified test was administered to 8 novice, 20 intermediate, and 14 expert participants in pediatric transfusion medicine. Data were analyzed using Rasch psychometric modeling to finalize test content. RESULTS: The finalized assessment tool included 25 multiple-choice questions, with balanced representation across the cognitive processes outlined in Bloom's taxonomy. Twenty-four questions demonstrated good fit statistics with infit average 0.99 (0.8-1.2) and outfit average 0.96 (0.78-1.22). One question required revision. Question difficulty ranged from -2.78 to 1.61 on the logit scale, with gaps observed for higher-ability test-takers. DISCUSSION: Developing the Pediatric Transfusion Knowledge Test represents a systematic approach to adapting and validating an assessment using a curriculum blueprint and ensuring alignment with educational materials. It adapts an existing assessment and incorporates evidence-based pedagogical principles. This framework can be applied to develop other assessments within medical education.
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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 | 0.033 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".