Achievement emotions within simulation in baccalaureate nursing education–A mixed methods study
Bibliographic record
Abstract
Objective: Simulation-based training equips students to meet the increasing demands of healthcare. While these trainings positively impact learning, the emotions experienced during simulations can influence these in learning outcomes. Achievement emotions, which are closely linked to academic performance, are considered to affect learning but have been underexplored in the context of simulation-based nursing education. Therefore, this study investigated the achievement emotions nursing students experience during simulation training and analyzed how they describe these emotions.Methods: A concurrent mixed-methods design was used. The Achievement Emotions Questionnaire was administered to a sample of nursing students (n = 101) assessing their emotions during simulation training. Additionally, 31 problem-centered interviews were conducted to delve deeper into the students' emotional experiences. Quantitative data were analyzed using IBM SPSS Statistics Version 28, while qualitative data were analyzed using content analysis following Kuckartz methodology, utilizing MAXQDA (Version 24.2.0) for coding and analysis.Results: Nursing students reported a range of achievement emotions, with positive emotions like enjoyment, pride, and hope scoring higher than negative emotions, such as boredom, hopelessness, and shame. Notably, anxiety levels were comparable to those of the positive emotions. Significant emotional shifts were observed during the simulation training. However, while quantitative data indicated a decrease in shame, interviews revealed students still felt shame after simulation, especially when knowledge gaps were exposed. Qualitative findings suggest that students' experience with simulation, the debriefing process, the training design, and their role in the simulation influence the achievement emotions experienced.Conclusions: The dynamic nature of achievement emotions during simulation training calls for further research to better understand their complexity. The discrepancy regarding shame between quantitative and qualitative findings also requires more investigation. Nursing educators should consider achievement emotions in simulation design, as factors like training structure influence students' emotional experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".