The Impact of Just-in-Time Simulation Training for Healthcare Professionals on Learning and Performance Outcomes: A Systematic Review
Bibliographic record
Abstract
ABSTRACT: Although just-in-time training (JIT) is increasingly used in simulation-based health professions education, its impact on learning, performance, and patient outcomes remains uncertain. The aim of this study was to determine whether JIT simulation training leads to improved learning and performance outcomes. We included randomized or nonrandomized interventional studies assessing the impact of JIT simulation training (training conducted in temporal or spatial proximity to performance) on learning outcomes among health professionals (trainees or practitioners). Of 4077 citations screened, 28 studies were eligible for inclusion. Just-in-time training simulation training has been evaluated for a variety of medical, resuscitation, and surgical procedures. Most JIT simulation training occurred immediately before procedures and lasted between 5 and 30 minutes. Despite the very low certainty of evidence, this systematic review suggests JIT simulation training can improve learning and performance outcomes, in particular time to complete skills. There remains limited data on better patient outcomes and collateral educational effects.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".