Do Childcare Teachers Evaluate Children’s Weight Status More Accurately Than Parents? A Brief Report
Bibliographic record
Abstract
Purpose Parents’ underestimation of young children’s weight can reduce their engagement and readiness to implement changes in children’s diet and physical activity. Childcare teachers can support parents’ identification of children at risk for being overweight only if they can accurately do this themselves. Design Quantitative, cross-sectional study. Setting Fifteen kindergartens near Lisbon, Portugal. Subjects 319 parents, 32 teachers (47.5% and 100% response rate, respectively), and 319 children. Measures Caregivers classified the children’s weight, considering their height and age as underweight, healthy weight, or overweight; children’s body mass index (BMI) status for age and sex was assessed. Analysis Differences in caregivers’ accuracy of children’s weight perception were assessed. Multilevel multivariate logistic regression models were used to analyze the predictors of the accuracy of teachers’ and parents’ weight perception as a binary outcome. Results The proportion of children with overweight correctly assessed differed significantly ( P = 0.004) between teachers (31.1%) and parents (17.5%). The child’s BMI percentile was the only significant positive predictor for both caregivers’ weight perception accuracy ( P < 0.001 and P = 0.004, for parents and teachers, respectively), holding the child’s age and sex constant. Conclusion Although childcare teachers were better raters than parents when evaluating children’s weight status, the percentage of children with overweight that childcare teachers misclassified was still relatively high.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".