Effectiveness of Patient Simulations in Dietetic Education and Training
Bibliographic record
Abstract
Widely used in teaching various healthcare students, patient simulations are not common in dietetics education. This mixed-methods study investigated effectiveness of patient simulations in two courses (one undergraduate, one graduate) in 2016 and 2017 in Applied Human Nutrition at the University of Guelph. Nutrition students acted as dietitians, and theatre students as patients. 99.8% of undergraduate and 82.6% of graduate nutrition students agreed/strongly agreed that simulations enhanced learning and confidence. Undergraduate students’ competence scores related to physical assessment, patient education, and communication skills improved by 46.9%, and graduate students’ scores related to assessment, patient education, communication and counselling skills, by 27.9% (both p < 0.01). Thematic analysis of students’ written reflections and focus group data suggested simulations increased communication and assessment skills, confidence and self-efficacy. Simulation realism, student preparedness, observing, post-simulation debriefing and reflecting increased perceived simulation value. Strengths, limitations, and clinical and pedagogical implications of simulation are discussed.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".