Impact of School-Based Interventions on Children’s Healthful Nutritional Outcomes (2009-2013): Implications for Future Research
Bibliographic record
Abstract
Objective: The objective was to review the literature on school-based interventions during compulsory education (published in 2009-2013) that may be effective in increasing healthful nutritional outcomes in children. Specific strategies and their limitations will be identified. Methods: A qualitative systematic analysis of seventeen school-based interventions applied to six-eleven year-old children, and published in peer-reviewed journals from 2009-2013 was carried out. Results: These studies taken as a whole used a variety of fourteen strategies, such as provision of nutritional and gardening education; repeated exposure to vegetables (V); peer and adult modelling; chefs going into schools to teach healthy nutritional issues. These studies showed four types of limitations: limitations derived from the experimental designs and experimental procedures used and limitations affecting participant samples and data collection. Fifteen recommendations for overcoming the targeted research limitations were identified, such as applying the intervention during longer time intervals; examining the effectiveness of these interventions in different ethnicities and socioeconomic groups; including larger participant samples; using randomized designs. Finally, six new working hypotheses to guide future studies are proposed here. For example, a small number of V offered might lead to insufficient opportunities to learn to like the flavour of V; schools’ previous experience with these interventions might distort children’s post-intervention data related to their nutritional outcomes; providing children with nutritional and gardening education might increase their fruit (F) selection, consumption and preferences and application of evaluative conditioning under more appropriate experimental conditions might increase child V consumption. Conclusion: seventeen school-based interventions were included. These studies taken as a whole used a variety of strategies (fourteen), and showed four types of limitations. Fifteen recommendations for overcoming the targeted research limitations as well as six new hypotheses are proposed here. Future research in this field should focus on overcoming its research limitations, such as the ones highlighted here, and building new working hypotheses, such as those proposed here.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".