Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator
Bibliographic record
Abstract
Background Frequent simulation-based education is recommended to improve health outcomes during neonatal resuscitation, but is often inaccessible due to time, resource, and personnel requirements. Digital simulation presents a potential alternative, however its effectiveness and reception by healthcare professionals (HCP) remains largely unexplored. Objectives This study explores HCPs’ attitudes towards a digital simulator, technology, and mindset to elucidate their effects on neonatal resuscitation performance in simulation-based assessments. Methods The study was conducted from April-August 2019, with 2-month (June-October 2019) and 5-month (September 2019-January 2020) follow-up at a tertiary perinatal centre in Edmonton, Canada. Of 300 available neonatal HCPs, 50 participated. Participants completed a demographic survey, pre-test, two practice scenarios using the RETAIN neonatal resuscitation digital simulation, post-test, and attitudinal survey (100% response rate). Participants repeated the post-test scenario in two-months (86% response rate) and completed another post-test scenario using a low-fidelity table-top simulator (80% response rate) five-months after the initial study intervention. Participants’ survey responses were collected to measure attitudes towards digital simulation, technology, and mindset. Knowledge was assessed at baseline (pre-test), acquisition (post-test), retention (2-month post-test), and transfer (5-month post-test). Results Fifty neonatal HCPs participated in this study (44 females and 6 males; 27 nurses, 3 nurse practitioners, 14 respiratory therapists, and 6 doctors). Most participants reported technology in medical education as useful and beneficial. Three attitudinal clusters were identified by a hierarchical clustering algorithm based on survey responses. Although participants exhibited diverse attitudinal paths, they all improved neonatal resuscitation performance after using the digital simulator and successfully transferred their knowledge to a new medium. Conclusions Digital simulation improved HCPs’ neonatal resuscitation performance. Medical education may benefit by incorporating technology during simulation training.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".