Simulation-based learning combined with debriefing: trainers satisfaction with a new approach to training the trainers to teach neonatal resuscitation
Bibliographic record
Abstract
BACKGROUND: Prompt initiation of appropriate neonatal resuscitation skills is critical for the neonate experiencing difficulty transitioning to extra-uterine life. The use of simulation training is considered to be an indispensable tool to address these challenges. Research has yet to examine the effectiveness of simulation and debriefing for preparation of trainers to train others on the use of simulation and debriefing for neonatal resuscitation. This study determines the degree to which experienced NRP instructors or instructor trainers perceived simulation in combination with debriefing to be effective in preparing them to teach simulation to other health care professionals. METHODS: Participants' perceptions of knowledge, skills, and confidence gained following a neonatal resuscitation workshop (lectures; scenario development and enactment; video recording and playback; and debriefing) were determined using a pre-post test questionnaire design. Questionnaire scores were subjected to factor and reliability analyses as well as pre- and post-test comparisons. RESULTS: A total of 17 participants completed 2 questionnaires. Principal component extraction of 18 items on the pre-test questionnaire resulted in 5 factors: teamwork, ability to run a simulation, skills for simulation, recognizing cues for simulation and ability to debrief. Both questionnaire scores showed good reliability (α: 0.83 - 0.97) and factorial validity. Pre- and post-test comparisons showed significant improvements in participants' perceptions of their ability to: conduct (as an instructor) a simulation (p < .05, η² .47); participate in a simulation (p < .05, η² .45); recognize cues (p < .05, η² .35); and debrief (p < .05, η² .41). CONCLUSIONS: Simulation training increased participants' perceptions of their knowledge, skills, and confidence to train others in neonatal resuscitation.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".