Association between Clinical Simulation Design Features and Novice Healthcare Professionals’ Cognitive Load: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Clinical simulations are complex educational interventions characterized by several design features, which have the potential to influence cognitive load, that is, the mental effort required to assimilate new information and learn. This systematic review and meta-analysis explored the associations between simulation design features and cognitive load in novice healthcare professionals. Methods Based on the Joanna Briggs Institute methodology, a search was performed in five databases for quantitative studies in which the cognitive load of novice healthcare professionals was measured during or after a simulation activity. Each clinical simulation was coded to describe its design features. Univariate and multivariate mixed model analyses were performed to explore the associations between simulation design features and cognitive load. Results From 962 unique records, 45 studies were included and 27 provided enough data on subjective cognitive load (i.e., Paas Scale and NASA-Task Load Index scores) to be meta-analyzed. In the multivariate analysis for the NASA-Task Load Index scores, each repetition of a simulation using the same scenario resulted in a linear decrease in cognitive load. In contrast, technology-based instruction before or during a simulation activity was associated with higher cognitive load. In the univariate analyses, other features such as feedback and instructor presence were also statistically associated with cognitive load. Regarding the univariate analyses of the Paas Scale scores, simulator type, briefing, debriefing, and repetitive practice were statistically associated with cognitive load. Conclusion This is the first meta-analysis exploring the relationship between clinical simulation design features and novice healthcare professionals’ cognitive load. Although the findings show that several design features can potentially increase or decrease cognitive load, several gaps and inconsistencies in the current literature make it difficult to appreciate how such reciprocity influences novice healthcare professionals’ learning. These limitations are discussed and avenues for educators and further research are suggested.
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".