Supporting Healthy Eating Among Rural Early Years Children: A Pulse Crop Pilot Intervention Study
Bibliographic record
Abstract
Dietary patterns are closely linked to healthy child development, which in turn is a powerful determinant of lifelong health and well-being. As many early years children spend a large part of their day in childcare centers, educators can have a large influence on their healthy eating behaviors. In Saskatchewan, a number of childcare centers are located in rural communities and research suggests that rural educators are influenced by unique factors when providing healthy eating opportunities for children. In order to diversify and improve the diets of rural early years children, a pulse crop intervention was piloted tested in one rural childcare center. A pre and post 28-week intervention design was used to evaluate the pulse crop intervention on increasing educators' knowledge and awareness about pulse crops and supporting educators in incorporating pulse crops into centers' menus. Overall, the intervention was effective in increasing rural educators' knowledge and use of pulse crops. Additionally, educators were able to diversify and increase the number of healthy meals offered in the childcare center. The lessons learned can be used to improve and expand the intervention to other childcare centers in Saskatchewan and beyond, in turn supporting the healthy development of early years children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".