DEVELOPMENT OF COMPETENCY ASSESSMENT TOOLS FOR NEONATAL BAG MASK VENTILATION AND INTUBATION: A DELPHI STUDY
Bibliographic record
Abstract
Abstract BACKGROUND As paediatric training programs shift to a competency-based education model, there is a growing need for tools with strong evidence of validity to teach and assess procedural skills. To date, there are no competency-based assessment tools for bag mask ventilation or neonatal intubation that are widely accepted in the field of paediatrics. OBJECTIVES We aimed to develop a neonatal bag mask ventilation competency assessment tool (BMVCAT) and neonatal intubation competency assessment tool (NICAT) to assess proficiency in these skills for application in both the clinical and simulation-based training environments. Delphi methodology was used to determine expert consensus regarding critical items to be included. DESIGN/METHODS Systematic literature reviews were performed to generate potential items to include in the assessment tools, consisting of two parts: a checklist of specific actions required to complete the procedures competently and global ratings reflecting overall competence on general aspects of the skill. Checklist items were grouped into 3 domains: pre-procedure, intra-procedure, and post-procedure. A Delphi panel of North American neonatal experts was established to determine expert consensus regarding critical items required to objectively assess the competence of individuals performing neonatal bag mask ventilation and intubation. Panelists completed iterative surveys to rate the importance of checklist and global rating items using a 7-point Likert scale. Responses were evaluated and items were removed after each round if the mean rating was <5.5 until consensus was achieved. RESULTS Thirty-four experts from 26 centres in Canada (N=23) and the United States (N=11) participated in the Delphi process: 18 neonatologists, 9 neonatal nurses or nurse practitioners, 4 respiratory therapists, 2 paediatricians, and 1 paediatric anesthesiologist. Systematic literature reviews generated 48 checklist items and 23 global rating items for the BMVCAT and 67 checklist items and 24 global rating items for the NICAT. The first Delphi round reduced the BMVCAT to 43 checklist items and 20 global rating items and the NICAT to 63 checklist items and 23 global rating items. The second Delphi round reduced the BMVCAT to 27 checklist items and 16 global rating items and the NICAT to 50 checklist items and 22 global rating items. The Delphi process was continued until expert consensus was achieved to generate the BMVCAT and NICAT. CONCLUSION Delphi methodology allowed for the determination of consensus regarding essential items to be included in tools designed to measure competence in performing neonatal bag mask ventilation and intubation. Further studies are planned to prospectively validate the BMVCAT and NICAT in clinical and simulated settings.
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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 | 0.148 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".