Quantifying Patient Interest in Plant-Based Diets in Primary and Specialty Medical Practices
Bibliographic record
Abstract
Introduction: Extensive research highlights the significant impact of plant-based diets (PBD) in reducing chronic disease rates and improving mortality outcomes. Despite these benefits, the implementation of PBD remains challenging due to multiple barriers faced by patients and their physicians. This study was designed to objectively evaluate patients’ interest in PBD and to explore opportunities for overcoming physician barriers. Methods: Twenty-nine physician practices in the United States and Canada were surveyed to evaluate patient interest in PBD. Colorful posters and nutrition cards outlining the benefits of PBD were placed in all exam rooms for 15-20 clinic days. Data on patient interactions, including the number of Quick Response (QR) code clicks to a webpage and the number of nutrition cards taken by patients, were collected. The percentage of each, relative to the total number of patient visits during the study period, was used to indicate patient interest in PBD. Physicians also completed pre- and post-study questionnaires. Results: A total of 10,508 patients from nine primary care and twenty subspecialty practices were included in this study. In the 29 individual physician practices, an average of 11.5% (range: 2% to 33.5%) of patients took a physical copy of the nutrition card, while 3.1% (range: 0% to 8%) scanned the QR code on a poster. Sixty-six percent of physician respondents felt their perception of patient interest in plant-based diets changed positively with the study. Physicians also felt the barriers to discussing PBD decreased after the study. Conclusion: Simple clinic-based interventions can promote plant-based nutrition for patients and physicians as well as aid in reducing physician barriers to doing so. Colorful visual tools such as posters with QR codes and nutrition cards led to measurable patient interest and the majority of physicians experienced favorable shifts in their perceptions of PBD. This information can be adapted across multiple specialties to support positive lifestyle changes in patients.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".