Mindset Moderates Healthcare Providers' Longitudinal Performance in a Digital Neonatal Resuscitation Simulator
Bibliographic record
Abstract
Background: Simulation education can benefit healthcare providers (HCPs) by providing opportunities to practice complex neonatal-resuscitation tasks in low-stake environments. To our knowledge, no study investigated the role of growth mindset on longitudinal performance on neonatal resuscitation before and after simulation-based training. Objectives: This study examines whether 1) the RETAIN digital/table-top simulators facilitate HCPs' neonatal resuscitation knowledge gain, retention, and transfer and 2) growth mindset moderates HCPs' longitudinal performance in neonatal resuscitation. Methods: Participants were n = 50 HCPs in a tertiary perinatal center in Edmonton, Canada. This longitudinal study was conducted in three stages including 1) a pretest and a mindset survey, immediately followed by a posttest using the RETAIN digital simulator from April to August 2019; 2) a 2-month delayed posttest using the same RETAIN neonatal resuscitation digital simulator from June to October 2019; and 3) a 5-month delayed posttest using the low-fidelity table-top neonatal resuscitation digital simulator from September 2019 to January 2020. Three General Linear Mixed Model (GLMM) repeated-measure analyses investigated HCPs' performance on neonatal resuscitation over time and the moderating effect of growth mindset on the association between test time points and task performance. Results: Compared with their pretest performance, HCPs effectively improved their neonatal resuscitation knowledge after the RETAIN digital simulation-based training on the immediate posttest ( Est = 1.88, p < 0.05), retained their knowledge on the 2-month delayed posttest ( Est = 1.36, p < 0.05), and transferred their knowledge to the table-top simulator after 5 months ( Est = 2.01, p < 0.05). Although growth mindset did not moderate the performance gain from the pretest to the immediate posttest, it moderated the relationship between HCPs' pretest and long-term knowledge retention (i.e., the interaction effect of mindset and the 2-month posttest was significant: Est = 0.97, p < 0.05). The more they endorsed a growth mindset, the better the HCPs performed on the posttest, but only when they were tested after 2 months. Conclusions: Digital simulators for neonatal resuscitation training can effectively facilitate HCPs' knowledge gain, maintenance, and transfer. Besides, growth mindset shows a positive moderating effect on the longitudinal performance improvement in simulation-based training. Future research can be conducted to implement growth-mindset interventions promoting more effective delivery of technology-enhanced, simulation-based training and assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".