School Food Practices of Prospective Teachers
Bibliographic record
Abstract
BACKGROUND: Schoolteachers can affect students' eating habits in several ways: through nutrition knowledge, positive role modeling, and avoidance of unhealthy classroom food practices. In this study, the knowledge, attitudes, and eating behaviors of prospective teachers as determinants of intended classroom food practices and the school environment and its potential impact on classroom food practices were examined and explored. METHODS: One hundred and three students (response rate 79%) enrolled in the final year of a bachelor of education program with at least 22 weeks of practice teaching completed a self-administered questionnaire adapted from the Teens Eating for Energy and Nutrition at School teaching staff survey. Indexes related to classroom food practices, school food environment, personal health, fat intake, and nutrition knowledge were constructed and explored quantitatively using linear modeling techniques and contingency table analysis. RESULTS: The majority of respondents reported a high fat intake (65%) and had mid-to-low nutrition knowledge (72%). While most respondents (93%) believed that a healthy school food environment was important, two thirds reported unhealthy classroom food practices. Unhealthy classroom food practices were more likely to be used by those intending to teach at the secondary level, those who held a high personal health belief, and those who demonstrated less support for a healthy school environment. CONCLUSIONS: These findings suggest that knowledge, attitudes, and food behaviors of prospective teachers may be barriers to promoting healthy food habits to their future students. Further, prospective teachers would benefit from policies and programs that support healthy classroom practices and from compulsory nutrition education in the teacher training curriculum.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".