Using Herbs and Spices to Increase Vegetable Intake Among Rural Adolescents
Bibliographic record
Abstract
OBJECTIVE: To test whether adding herbs and spices to school lunch vegetables increases selection and intake compared with lightly salted control versions among rural adolescents. DESIGN: This study compared intake of vegetables with herbs and spices with lightly salted controls (phase I) and tested whether 5 repeated exposures would increase students' intake of herb and spice seasoned vegetables (phase II). PARTICIPANTS AND SETTING: A total of 600-700 students at a rural middle/high school (age 11-18 years). INTERVENTION: In phase I, herbs and spices were added to 8 vegetables and outcomes were compared with 8 control recipes. In phase II, the impact of repeated exposure to herb and spice blends served on different vegetables was assessed. MAIN OUTCOMES: Vegetable selection rates, weighed intake, and willingness to eat again. ANALYSIS: Two-way ANOVAs tested effects of condition (herbs and spices vs control; before vs after exposure) and age (middle vs high school) on selection and intake. RESULTS: In phase I, students ate more control than seasoned broccoli (P = .01), cauliflower (P = .006), and green beans (P = .01), and high schoolers generally consumed more seasoned vegetables than did middle schoolers (P < .03). In phase II, repeated exposure to herbs and spices increased reported willingness to eat again for seasoned broccoli (P = .003). CONCLUSIONS AND IMPLICATIONS: In a short-term intervention, herbs and spices did not produce robust increases in school lunch vegetable intake among rural adolescents, but limited repeat exposure may increase students' willingness to consume these flavors. Additional work is needed to identify individual and school-level characteristics that affect students' willingness to select and consume vegetables with herbs and spices.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".