Using High‐fidelity Human Patient Simulation to Teach Functional Aerodigestive Tract Anatomy And Bridge Clinical Skills: Student Perceptions of Clinical Preparedness
Bibliographic record
Abstract
Introduction & Objective High‐fidelity human patient simulation (HPS) improves emerging clinical competencies in medical training programs. HPS simulates complex learning environments while minimizing risks. Limited evidence is available regarding HPS use in aerodigestive tract (AT) disorders. Our objective was to explore clinical graduate students’ perceptions regarding their knowledge, skills, and confidence in functional AT anatomy across three different learning environments: didactic, standardized patient (i.e., role play), and HPS. Materials & Methods Using a cross‐over design, and following completion of didactic head and neck anatomy lectures, graduate students in a speech‐language pathology clinical program were randomly assigned to one of two case‐based scenarios: standardized patient (STP) or HPS. Following the completion of their assigned scenario, students then completed the alternate. A self‐administered survey using a 10‐pt Likert scale (1 = none/no, 10 = extreme/strongly agree) rated student perceptions across domains of knowledge (K), skills (S) and confidence (C) at four time points (baseline, after didactic, STP, and HPS). Between and within subject comparisons were conducted using the repeated measures. For each domain (K, S, and C), separate and aggregate group mean differences were calculated comparing the domains using t‐test with Welch modification (assuming unequal variance). Within subject changes following each learning environment were calculated using Pearson product moment. Statistical significance was p< .05. Results A total of 66 (N) students participated with 227 surveys completed (50% response rate). Across participants, mean (SD) baseline knowledge (4.6 [2.3]), skills (3.6 [2.3]) and confidence (3.2 [2.0]) significantly improved to 7.7 (1.2), 7.1 (1.5) and 6.7 (1.7) respectively following all learning environments. Significant overall increase across K, S and C (aggregate) was 1.5 and strongly correlated to didactic lecture r (35) = .76 (p<.001) and the completion of one scenario r (27) = .84 (p<.001). When comparing STP and HPS, significant improvements were reported in the areas of knowledge (+0.4, p<.05) and confidence (+0.3, p<.05) only with strong correlation to HPS (K= r (23) = .80, p<.001; C = r (23) = .90, p<.001). The majority strongly agreed that both STP and HPS are extremely useful (9.3 [1.2]). Overall, mean (SD) perceived clinical reasoning increased from 3.7 (2.2) to 6.7 (1.1) with significant improvements following didactic (p<.01) at least one case scenario (p<.001), however no significant differences were observed when comparing STP to HPS. Students rated the value of both STP and HPS highly (9.4 [1.2] vs. 9.3 [1.1]). Conclusion & Significance Students perceived that multiple learning environments enhance their knowledge, skills and confidence in AT disorders. While they determined that STP and HPS are equally valuable, their knowledge and confidence improved significantly following HPS. Clinical skills did not change between STP and HPS. This study was the first to develop HPS modules while incorporating functional AT anatomy with clinical skills training across didactic and STP environments. Student learning and confidence is maximized through these environments and using HPS is feasible and effective in our clinical training program.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".