Effect of an Integrated Case-based Nutrition Curriculum on Medical Education at Qazvin University of Medical Sciences, Iran
Bibliographic record
Abstract
INTRODUCTION: Nutrition education is identified as an important part of medical education by organizations. Qazvin University of Medical Sciences, school of medicine (QUMS SOM), has a required basic nutrition course of 36 hr in second year of medical school, but education experts reports show that the course does not provide required therapeutic skills for graduate student. METHOD: We decided to organize an 8-hr diet therapy work shop in order to develop a patient experience clinical based case study teaching to enhance clinical skills at QUMS SOM. RESULTS: Students' perception score about adequacy of nutrition instruction increased from 20% (at baseline) to 70% (after intervention). The mean nutrition knowledge score of total students in clinical nutrition were different between before and one month after integration (8.3±2.5, 13.4±3.2, P < 0.001). And two groups of participants including staggers and interns had similar nutritional knowledge score at pre-test (7.9±2.6 and 8.9±2.3 respectively). CONCLUSION: Implemented nutrition curriculum at QUMS was appropriate method to enhance student's perception about nutrition integration and to increase and translate the knowledge to clinical practice.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".