A Systematic Review of Methods to Assess Children's Diets in the School Context
Bibliographic record
Abstract
To evaluate the impact of school-based nutrition interventions, accurate and reliable methods are needed to assess what children eat at school. The primary objective of this study was to systematically review methodological evidence on the relative accuracy and reliability of dietary assessment methods used in the school context. The secondary objective was to assess the frequency of methods and analytical approaches used in studies reporting in-school dietary outcomes. Three health databases were searched for full-text English-language studies. Twenty-two methodological studies were reviewed. For school meal recalls, the majority of studies (n = 8 of 12) reported poor accuracy when accuracy was measured by using frequencies of misreported foods. However, when energy report rates were used as a measure of accuracy, studies suggested that children were able to accurately report energy intake as a group. Results regarding the accuracy of food-frequency questionnaires (FFQs) and food records (FRs) were promising but limited to a single study each. Meal observations offered consistently good interrater reliability across all studies reviewed (n = 11). Studies reporting in-school dietary outcomes (n = 47) used a broad range of methods, but the most frequently used methods included weighed FRs (n = 12), school meal recalls (n = 10), meal observations by trained raters (n = 8), and estimated FRs (n = 7). The range of dietary components was greater among studies relying on school meal recalls and FRs than among studies using FFQs. Overall, few studies have measured the accuracy of dietary assessment methods in the school context. Understanding the methodological characteristics associated with dietary instruments is vital for improving the quality of the evidence used to inform and evaluate the impact of school-based nutrition policies and programs.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".