The Impact of Simulation-Based Spaced Training for Skills Acquisition on Learning and Performance Outcomes Among Healthcare Professionals
Bibliographic record
Abstract
Spaced learning is increasingly used in simulation-based education, yet its impact on learning, performance, and patient outcomes is unclear. We compared spaced training (several discrete sessions) with massed training (a single session) for skills acquisition in health professionals. We systematically reviewed randomized or prospective comparative studies. Of 4572 citations screened, 15 met inclusion criteria. Studies covered resuscitation and surgical procedures, most with spacing intervals of about 1 week. Despite heterogeneity in study design, participants, and outcomes, spaced training was generally as effective as massed training. Some evidence suggested advantages for spaced training in skill retention, particularly for time to complete procedures. Findings were inconsistent across other outcomes. No studies demonstrated improvements in patient care practices, patient outcomes, or broader educational effects. These results suggest spaced simulation may offer retention benefits for certain skills, but more research is needed to assess its impact on clinical and system-level outcomes.
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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 | 0.019 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".