A distributed learning strategy improves performance and retention of skills in neonatal resuscitation
Bibliographic record
Abstract
Skill retention after neonatal resuscitation training is poor. A distributed learning strategy – where learning is spread over multiple sessions – can improve retention of declarative memory (facts & knowledge). Session timings are critical – maximal retention occurs when a refresher session is scheduled at 10-30% of the time between initial training and test. We hypothesized this also holds true for neonatal resuscitation, a complex skill set requiring both declarative and procedural memory. We conducted a prospective, single-blinded randomized-controlled trial. University of Ottawa residents were recruited to training in neonatal resuscitation, with a high-fidelity simulated pre-test, immediate post-tests, and a retention test at 4 months. After training, they were randomized to either a refresher session at 3 weeks (18% of interval) or at 2 months (50%). Technical and non-technical skills were scored using validated checklists, knowledge with standardized questions. There was no difference between groups prior to the retention test. The early refresher group demonstrated significantly improved technical (mean ± 95% CI: 22.4 ± 1.3 v 18.2 ± 2.5, p = 0.02) and non-technical (31.0 ± 0.9 v 25.6 ± 3.1, p = 0.03) skill scores in the retention post-test compared to the late group. No difference was seen with knowledge scores. We conclude that both technical and non-technical aspects of neonatal resuscitation performance can benefit from an early refresher session. Session timings are critical and should be tailored to the desired length of skill retention. Findings may be generalizable to other interventions that depend on mixed types of memory.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".