Board 187 - Program Innovations Abstract Student Perceived Influences and Hindrances to Learning in the Simulated Environment and Traditional Clinical Experiences (Submission #826)
Notice bibliographique
Résumé
Introduction/Background Nursing programs are increasing the use of varying levels of fidelity simulation across the curriculum while preparing students to become professional nurses.5 The nursing education community realizes the new generation students’ ability to adapt to technology and sees value in using simulation to supplement education.7,9 Studies have been conducted to examine factors that influence learning outcomes in simulation2,6; however, there is nogeneral agreement on when and how to use the simulation technology.1,10 Persistent calls for additional rigorous empirical research are present in the literature.3,8 However, there is a lack of research comparing student perceived effectiveness of motivational factors in simulation experiences compared to traditional clinical experiences. This study examined what factors motivate students to learn from the simulations. Factors identified were technology, interaction and participation, mentor’s inspiration, facilitating conditions or catalyst, and hindrances based on a study by Mashaw et al.,4 on online learning. This study suggested that educators need to identify key motivational factors through research and design their learning experiences accordingly. Methods A survey was adapted from Mashaw and colleagues4 for measuring the identified factors applied to student’s perceived effectiveness for motivating learning in both the clinical and traditional clinical experiences. Students (n = 120) from three nursing courses that offered varying contents at different levels worked on high fidelity simulations and clinical environments. Students either participated in their traditional clinical experiences first then simulation or simulation then clinical experiences. Students completed the survey upon completion of their simulation experience and traditional clinical experiences. The simulations were generally viewed as positive learning experiences. The facilitating conditions, as well as interaction and participation factors, significantly influenced the student’s learning experiences in simulations. Facilitating conditions and removing hindrances significantly influenced the students’ learning experiences in clinical environments. Results: Conclusion Results suggest that by clearly stating the objectives, inspiring the students, removing hindrances and encouraging interactive participation, instructors might improve their students’ learning experiences in simulations and clinical environments. References 1. Cant, R. P., & Cooper, S. J. (2010). Simulation-based learning in nurse education: Systematic review. Journal of Advanced Nursing, 66(1), 3-15. doi:10.1111/j.1365-2648.2009.05240.x. 2. Jeffries, P. R. (Ed.). (2007). Simulation innursing education: From conceptualization to evaluation. New York, NY: National League of Nursing. 3. LaFond, C. M., & Catherine. (2013). A critique of the National League for Nursing/Jeffries simulation framework. Journal of Advanced Nursing, 69(2), 465-480. doi:http://dx.doi.org.spot.lib.auburn.edu/10.1111/j.1365-2648.2012.06048.x. 4. Mashaw, B. (2012). A model for measuring effectiveness of an online course. Decision Sciences Journal of Innovative Education, 10(2), 189-221. doi:10.1111/j.1540-4609.2011.00340.x. 5. Nehring, W. M. (2010). History of simulation in nursing. In Nehring, W. M. & Lashley, F. R. (Eds.). High-fidelity patient simulation in nursing education (pp. 3-26). Sudbury, MA: Jones and Bartlett Publishers. 6. Reed, S. J. (2012). Debriefing experience scale: Development of a tool to evaluate the student learning experience in debriefing. Clinical Simulation in Nursing, 8(6), e211-e217. 7. Robin, B. R., McNeil, S. G., Cook, D. A., Agarwal, K. L., & Singhal, G. R. (2011). Preparing for the changing role of instructional technologies in medical education. Academic Medicine, 86(4), 435-441. 8. Schiavenato, M. (2009). Reevaluating simulation in nursing education: Beyond the human patient simulator. The Journal of Nursing Education, 48(7), 388-394. 9. Skiba, D. J., Connors, H. R., & Jeffries, P. R. (2008). Information technologies and the transformation of nursing education. Nursing Outlook, 56(5), 225-230. 10. Weaver, A. (2011). High-fidelity patient simulation in nursing education: An integrative review. Nursing Education Perspectives, 32(1), 37-40. Disclosures Auburn University Intramural Grant Program Level III.
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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,322 | 0,052 |
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 ».