<p>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</p>
Bibliographic record
Abstract
This review aimed primarily to investigate the current trends of overweight and obesity in school children in the African context, secondly to explore the contribution of home and school environments on the children's food choices and lastly suggesting measures for creating a healthier food environment. Despite the increase in overweight and obesity among school children, empirical evidence on their determinants in the African context is scarce, thus calls for consideration of home and school environments. A literature search was conducted between October and December 2018 using Medline (PubMed), Directory of Open Access Journals, Google Scholar, manual search and "grey" literature. This review included articles published between the 1st January 2008 and 30th June 2018. Out of 343 articles, 49 were included for the full text reading after meeting the inclusion criteria. Five reports from grey literature were also included. Results show that the prevalence of overweight and obesity among school children in Africa is increasing and ranges from <5% to >40% in the 10-year period in which the review was taken. High socio-economic status, urban residence and female gender predicted higher prevalence of overweight/obesity. Few reviewed articles on the contribution of home and school environments on children's food choices showed a shred of evidence, thus calls for further research to address this gap. This review found an increasing prevalence of overweight and obesity in school children in Africa. Therefore, further investigation of home and school environment is imperative to curb the increase in the magnitude of overweight and obesity.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".