Growth Mindset Moderates the Effect of the Neonatal Resuscitation Program on Performance in a Computer-Based Game Training Simulation
Bibliographic record
Abstract
This study examines for the first time the moderating role of growth mindset on the association between the time elapsed since participants’ last refresher neonatal resuscitation program (NRP) course and their performance on neonatal resuscitation tasks in the RETAIN computer game training simulation. Participants were n = 50 health-care providers affiliated with a large university hospital. Results revealed that growth mindset moderated the relation between participants’ task performance in the game and the time since their latest refresher NRP course. Specifically, participants who completed the course more recently (i.e., between eight and nine months before the current study) made significantly more mistakes in the game than participants who completed the course more than nine months prior to the present study but only when they endorsed lower levels of growth mindset. Implications of this research include growth mindset interventions and increased screen time in simulation sessions that have the potential to help health-care providers achieve better performance on neonatal resuscitation clinical tasks.
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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 | 0.001 | 0.009 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".