Using High‐fidelity Human Patient Simulation to Teach Functional Aerodigestive Tract Anatomy And Bridge Clinical Skills: Student Perceptions of Clinical Preparedness
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».