Educational Actions at School: Proposal to Increase Children’s Contact with Vegetables
Bibliographic record
Abstract
The objective of the research was to evaluate the effect of educational actions in the school environment on the level of food neophobia, knowledge, consumption, acceptance, frequency of intake, planting of vegetables and assistance in cooking preparations among children. In addition, to verify the impact of actions on the sensory acceptance of food products added with vegetables with low acceptability by this public. Eighty-six children aged 7 to 10 years participated. The research was organized in three stages: pre-intervention, with filling out questionnaires and sensory analysis of the products; intervention, with application of educational actions and; post-intervention, with reapplication of questionnaires and sensory analysis of products. Actions included the implementation of vegetable gardens, theoretical-practical activities and cooking workshops. The physicochemical composition of the products was carried out to ensure food safety. Educational actions reduced the degree of food neophobia and improved the acceptability of food products by children (p < 0.05). In general, the educational actions had a positive impact (p < 0.05) on the participants’ knowledge, consumption, acceptance and frequency of vegetables intake However, there was little influence to increase the planting of vegetables at home, with no change in helping children with cooking preparations (p > 0.05). The food products presented a good nutritional profile. It is concluded that educational actions carried out at school are efficient to reduce food neophobia and increase knowledge, consumption, acceptance and frequency of intake among children. Also, they improve the acceptability of food products with the addition of vegetables with low acceptance by this public.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".