Factors influencing nutrition education in elementary schools: A qualitative study
Bibliographic record
Abstract
Background: Curriculum-based nutrition education is a common strategy to build food literacy skills and promote lifelong healthy eating. While past research has reported on the barriers teachers face when teaching about nutrition, very few recent studies have been published on the topic. This paper aimed to explore some of the views, barriers and facilitators that teachers face when teaching nutrition in elementary schools. Methods: A descriptive qualitative research study was conducted among elementary school teachers in Ontario, Canada, using a semi-structured interview guide. The guide was informed by the Consolidated Framework for Implementation Research, and then validated and pilot tested. Two independent researchers inductively coded and thematically analysed the transcripts. Results: = 9) indicated that nutrition was an important part of their teaching. Barriers to teaching nutrition included insufficient training/professional development opportunities, limited instructional time, competition with other subjects deemed a higher priority, limited financial resources to pay for teaching materials, lack of French language resources and sensitivities related to culture, socioeconomic status, and eating disorders. In contrast, interactive learning activities, integrating nutrition with other subjects, and support from school leadership, parents and professional organisations were seen as facilitators of nutrition education. Conclusions: Enhancing teacher training, resources and support while prioritising nutrition in the school curriculum is crucial for effective and equitable nutrition education in Canadian elementary schools. Addressing barriers and leveraging the facilitators identified in this study are essential to improve curriculum-based nutrition education provision.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".