Nutrition Education in Primary Care: Comparing Video vs Handout Interventions
Bibliographic record
Abstract
OBJECTIVE: Compare the effectiveness of instructional videos with print handouts when educating family medicine patients about the use of herbs and spices to reduce sodium, saturated fat, and added sugars during meal preparation. DESIGN: Enrollees were randomized to either view 5 short videos or read 3 handouts. The intervention was implemented while patients waited for their provider to begin their appointment. Postintervention surveys were completed on the patient's smartphone. SETTING: Penn State Health family medicine clinics. PARTICIPANTS: Patients who attended in-person appointments between September 2022 and August 2023 (n = 102). MAIN OUTCOME MEASURE(S): The impact of video and handout intervention on participants' interest, confidence, knowledge, and intention to use herbs and spices and their perceptions of the intervention. ANALYSIS: Descriptive statistics summarized sample characteristics; t tests compared video and handout groups. RESULTS: The video group had higher scores for interest, confidence, and intention to use herbs and spices. Participants perceived the videos as clearer (P = 0.001) and more appropriately complex (P = 0.02) than the handout materials. CONCLUSIONS AND IMPLICATIONS: Videos were superior to handouts in promoting interest, confidence, and intention to use herbs and spices for healthier cooking. Videos may improve patient engagement and preventive health care practices in clinical settings.
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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 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".