Nutrition knowledge competencies of intermediate and senior phase educators in Limpopo Province
Bibliographic record
Abstract
Background: Children’s food preferences and willingness to try new foods are influenced by the people around them, including families and teachers. The eating behaviours children practise early in life may continue to shape their food attitudes and eating patterns through adulthood.Aim: The purpose of this study was to explore the nutrition knowledge competencies of educators in primary schools.Setting: This study was conducted in Makhuduthamaga local municipality, Limpopo Province, South Africa.Methods: This study adopted a quantitative, descriptive and exploratory research design. A simple random sampling technique was used to select 30 primary schools and purposively select 200 educators responsible for Grades 5–7. The data were analysed using the Statistical Package for Social Science (SPSS), version 21.Results: Of the 200 educators, 66.5% were women and 34% were trained at a college and had teaching experience of between 21 and 30 years. Most of the educators knew the importance of carbohydrates, fat, vegetables and fruits. Only a quarter (26%) of educators knew the importance of protein, although 75.5% knew that protein forms part of a balanced diet. The overall knowledge score revealed that 92% of the educators had a poor knowledge score. There was no significant difference among selected socio-demographic characteristics, such as level of education (p = 0.129), training institution (p = 0.534) and nutrition knowledge (p 0.05).Conclusion: The overall nutrition knowledge of educators was poor, with about half of the educators reporting that their training was the main determinant of their nutrition knowledge. Therefore, there is a need for the incorporation of nutrition content into the training curriculum of educators.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".