Presimulation Instruction of Technical Skills to Enhance Simulation-Based Education of Non-Technical Skills: A Convergent Mixed Method Study
Bibliographic record
Abstract
INTRODUCTION: Teamwork practice through simulation-based education (SBE) is effective, but optimal instructional design remains uncertain. Preinstruction targeting technical skills (TS) and non-TS (NTS) has shown promise in supporting their respective acquisition through simulation. However, evidence remains limited on whether preteaching TS can enhance NTS acquisition, such as crisis resource management (CRM). This study aims to assess the impact of presimulation instruction of TS on the acquisition of CRM during SBE. METHODS: We used a convergent mixed-method design, combining a quantitative post-test-only control group design with a complementary qualitative component. The intervention group had access to preinstruction of the TS necessary for managing an acutely ill patient, whereas the control group was exposed to a sham video. The main outcome was CRM skills acquisition, as measured by the Ottawa Global Rating Scale (OGRS) after 2 SBE sessions held 3 months apart (T 0 and T 3 ). Secondary objectives were the intervention's effect on anxiety, cognitive load, and participants' perceptions of the intervention. Quantitative outcomes were assessed with a repeated-measures general linear model. Semistructured interviews were conducted after each simulation, and thematic analyses were performed. RESULTS: Sixty-four postgraduate year 1 (PGY1) residents were randomized into intervention and control groups. Participants who received preinstruction of TS in addition to SBE of NTS achieved significantly higher overall OGRS scores than those who received SBE of NTS alone. There were no between-group differences in anxiety measures. Qualitative analysis revealed high variability in the intervention's impact on participants, some revealing lower cognitive load, whereas others heightened levels of performance anxiety. CONCLUSIONS: In PGY1 residents, preinstruction of TS may reduce cognitive load during simulation training and enhance CRM skill acquisition at 3 months, although not via anxiety reduction. Responsiveness to the study intervention was variable and highlights the need for further research on the impact of instructional design adaptations on different learner subsets.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".