The Development and Testing of a Performance Checklist to Assess Neonatal Resuscitation Megacode Skill
Bibliographic record
Abstract
PURPOSE: The purpose of this work was to develop and assess the feasibility, reliability, and validity of a brief performance checklist to evaluate skills during a simulated neonatal resuscitation ("megacode") for the Neonatal Resuscitation Program of the American Academy of Pediatrics. METHODS: A performance checklist of items was created, validated, and modified in sequential phases involving: an expert committee, review, and feedback by Neonatal Resuscitation Program instructors for feasibility and criticality and use of the performance checklist by Neonatal Resuscitation Program instructors reviewing videotaped megacodes. The final 20-item performance checklist used a 3-point scale and was assessed by student and instructor volunteers. Megacode scores, the NRP multiple-choice examination scores, student assessments of their ability and performance, and sociodemographic descriptors for both students and instructors were collected. Data were analyzed descriptively. In addition, we assessed the megacode score internal consistency reliability, the correlations between megacode and multiple-choice examination scores, and the variance in scores based on instructor and student characteristics. RESULTS: A total of 468 students and 148 instructors volunteered for the study. The instrument was reliable and internally consistent. Student's scores were high on most items. There was a significant but low correlation between the megacode score and the written knowledge examination. Instructor and student characteristics had little effect on the variance in scores. CONCLUSIONS: This performance checklist provides a feasible assessment tool. There is evidence for its reliability and validity.
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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 | 0.023 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".