Accuracy of Parental Reporting of Preschoolers’ Dietary Intake Using an Online Self-Administered 24-h Recall
Bibliographic record
Abstract
Parents are typically relied upon to report young children’s dietary intake. However, there has been limited research assessing the accuracy of such reports captured using novel dietary assessment tools. The purpose of the current study was to assess the validity of the web-based Automated Self-Administered 24-h Dietary Assessment Tool (ASA24-Canada) for capturing dietary intake among children aged two-five years (n = 40), using parental proxy reporting. The study was conducted in a daycare setting, allowing for standardization of foods and drinks offered and direct observation of intake. Parental-reported intake was compared to true intake for lunch and dinner, as well as an afternoon snack, on a single day. Each eating occasion, including plate waste, was unobtrusively documented. Parents were not present for lunch or the afternoon snack, but joined their children at the daycare centre for the dinner meal. The following day, parents reported their children’s intake from the previous 24-h period using ASA24-Canada. For the eating occasions assessed, parents reported exact or close matches for 79.2% (82.3% for lunch, 81.2% for the snack, and 77.4% for dinner) of the foods and beverages truly consumed by children. Estimates of intake for energy and macronutrients examined (carbohydrates, fat, and protein) based on parental reports were higher than those based on true (observed) intake. Our findings suggest that parents are able to report what their preschool children eat and drink relatively accurately. However, the accuracy of portion size estimates is low. Strategies to enhance portion size reporting are needed to improve parental proxy reporting.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".