Contribution of home and school environment in children’s food choice and overweight/obesity prevalence in African context: Evidence for creating enabling healthful food environment
Bibliographic record
Abstract
Abstract Background Informed dietary choices during childhood is necessary for building good eating habits in the present and future generations. There is a significant increase globally in trends of over nutrition, specifically, overweight and obesity among school children in Africa calls for consideration of home and school environments. Methods A systematic literature search was conducted between October to December 2018 using Medline (PubMed), Directory of Open Access Journals and Google Scholar databases. Also, a grey literature review was conducted to identify and retrieve relevant documents and reports some of which from websites of international organizations. Major topics of interest were home and school food environments, dietary choices, school children and Africa. Out of 318 articles 30 were included in the full text read after meeting the inclusion criteria such as focusing on school children in Africa. Four reports from grey literature were also included. This review includes articles published between the 1st January 2008 and 30th June 2018. Results Available data from reviewed articles showed that obesity prevalence among school children in Africa is on the rise and ranges from less than 5% to more than 30% across countries. Few articles investigated the contribution of home and school environments on school children’s food choices which necessitates more research in this area. Conclusion Therefore, this review suggests that for effective implementation of childhood overweight and obesity reduction strategies, investigation of home and school determinants of children’s food choices is imperative.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".