Evaluating the Influence of Videos on Medical Professionals’ Perception of Using Herbs and Spices for Healthy Cooking and Potential Application in Patient Care
Bibliographic record
Abstract
OBJECTIVE: To assess medical students and primary care providers' interest, knowledge, confidence, and intent to incorporate herbs and spices in cooking using nutrition education videos and evaluate their perception about using the videos for patient care. METHODS: Before the intervention, participants were surveyed on their interest, knowledge, and confidence in using herbs and spices in cooking. Participants then viewed the nutrition education videos. After, participants were surveyed on the same measures as the initial survey, their perceptions of the videos and their usability with patients in clinical settings. RESULTS: All participants reported an increase in interest, knowledge, confidence, and intent variables after watching the videos (P < 0.05). CONCLUSIONS AND IMPLICATIONS: Overall, the videos were rated highly and usable within a clinical setting. They also facilitated nutritional education among medical professionals and their patients. Future research should determine the feasibility of implementing similar materials in clinics.
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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 | 0.003 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".