Board 187 - Program Innovations Abstract Student Perceived Influences and Hindrances to Learning in the Simulated Environment and Traditional Clinical Experiences (Submission #826)
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.322 | 0.052 |
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".