Dietetic Students’ Perceived Anxiety towards Simulation Activities: A Mixed-methods Pilot Study
Bibliographic record
Abstract
Purpose: To assess dietetic students’ anxiety levels before and after a series of simulations and to document students’ perceived sources of anxiety while completing simulation-based learning activities. Methods: A mixed-method convergent pilot study was conducted. Students enrolled in a Nutrition Assessment course that included a series of four simulations were invited to participate. Students completed an online pre-post simulation survey and engaged in individual interviews and a focus group discussion. The questionnaires included demographic questions and the French State-Trait Anxiety Inventory. Nonparametric tests and thematic analysis were used to examine data. Results: Fourteen students participated in the study’s quantitative component and seven in the qualitative component. There was a significant decrease in trait (44.5 ± 13.8 vs 32.0 ± 14.0, P = 0.01) and state (47.0 ± 11.2 vs 33.0 ± 18.0, P = 0.05) anxiety from pre- to post-simulations. Individual factors influencing students’ anxiety levels were stress and self-confidence. External factors influencing students’ anxiety levels were unknown situations, observers, patient profiles, observers’ feedback, preparation level, and academic setting. Conclusions: Our findings suggest that ensuring the educator is not present during simulations, advance preparation, and reassurance that simulations are a learning and not an evaluation activity may be effective pedagogical strategies for dietetic educators to reduce learners’ anxiety and facilitate competency development.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".