Instructional design features in ultrasound‐guided regional anaesthesia simulation‐based training: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Ultrasound-guided regional anaesthesia enhances pain control, patient outcomes and lowers healthcare costs. However, teaching this skill effectively presents challenges with current training methods. Simulation-based medical education offers advantages over traditional methods. However, the use of instructional design features in ultrasound-guided regional anaesthesia simulation training has not been defined. This systematic review aimed to identify and evaluate the prevalence of various instructional design features in ultrasound-guided regional anaesthesia simulation training and their correlation with learning outcomes using a modified Kirkpatrick model. METHODS: A comprehensive literature search was conducted including studies from inception to August 2024. Eligibility criteria included randomised controlled trials; controlled before-and-after studies; and other experimental designs focusing on ultrasound-guided regional anaesthesia simulation training. Data extraction included study characteristics; simulation modalities; instructional design features; and outcomes. RESULTS: Of the 2023 articles identified, 62 met inclusion criteria. Common simulation modalities included live-model scanning and gel phantom models. Instructional design features such as the presence of expert instructors, repetitive practice and multiple learning strategies were prevalent, showing significant improvements across multiple outcome levels. However, fewer studies assessed behaviour (Kirkpatrick level 3) and patient outcomes (Kirkpatrick level 4). DISCUSSION: Ultrasound-guided regional anaesthesia simulation training incorporating specific instructional design features enhances educational outcome; this was particularly evident at lower Kirkpatrick levels. Optimal combinations of instructional design features for higher-level outcomes (Kirkpatrick levels 3 and 4) remain unclear. Future research should standardise outcome measurements and isolate individual instructional design features to better understand their impact on clinical practice and patient safety.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".