Pulse-Based Nutrition Education Intervention Among High School Students to Enhance Knowledge, Attitudes, and Practices: Pilot for a Formative Survey Study
Bibliographic record
Abstract
BACKGROUND: Promoting pulse consumption in schools could improve students' healthy food choices. Pulses, described as legumes, are rich in protein and micronutrients and are an important food choice for health and well-being. However, most Canadians consume very little pulse-based food. OBJECTIVE: This pilot study sought to investigate outcomes of a teacher-led, school-based food literacy intervention focused on the Pulses Make Perfect Sense (PMPS) program in 2 high schools in Saskatoon, Saskatchewan. METHODS: Both high schools were selected using a convenience sampling technique and have similar sociodemographic characteristics. The mean age of students was 16 years. The intervention comprised 7 key themes focused on pulses, which included defining pulses; health and nutritional benefits of pulses; incorporating pulses into meals; the role of pulses in reducing environmental stressors, food insecurity, and malnutrition; product development; taste testing and sensory analysis; and pulses around the world. A self-administered questionnaire was used to assess knowledge, attitudes, practices, and barriers regarding pulse consumption in students at baseline and study end. Teachers were interviewed at the end of the intervention. Descriptive statistics and the nonparametric Mann-Whitney U test were used for analysis. RESULTS: In total, 41 and 32 students participated in the baseline and study-end assessments, respectively. At baseline, the median knowledge score was 9, attitude score was 6, and barrier score was 0. At study end, the median knowledge score was 10, attitude score was 7, and barrier score was 1. A lower score for barriers indicated fewer barriers to pulse consumption. There was a significant difference between baseline and study-end scores in knowledge (P<.05). Barriers to pulse consumption included parents not cooking or consuming pulses at home, participants not liking the taste of pulses, and participants often preferring other food choices over pulses. The teachers indicated that the pulse food-literacy teaching resources were informative, locally available, and easy to use. CONCLUSIONS: Despite the improvements in knowledge, attitude, and practice, pulse consumption did not change significantly at the end of the intervention. Future studies with larger samples are needed to determine the impact of PMPS on knowledge, attitude, and practice of high school students.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".