Evaluation of a child food reward task and its association with maternal feeding practices
Bibliographic record
Abstract
Food reward is defined as the momentary value of a food to the individual at the time of ingestion and is characterised by two psychological processes-"liking" and "wanting". We aimed to validate an age-appropriate food reward task to quantify implicit wanting of children from the GUSTO cohort (n = 430). At age 5 years, child appetitive traits and maternal feeding practices were reported by mothers via questionnaires. At age 6, a write-for-food task based on the child's preference for food or toy rewards was undertaken in laboratory conditions. Child BMI and skinfold measurements were taken at age 7. Convergent validity of the food reward task was assessed by associating with child appetitive traits, where enjoyment of food/food responsiveness (OR: 1.51; 95% CI: 1.06, 2.15) and emotional overeating (OR: 1.64; 95% CI: 1.09, 2.48) were positively associated with high food reward in children. Criterion validity was tested by associating with child BMI, however no significant relationships were observed. Multivariable logistic regression analysis with maternal feeding practices revealed that children whose mother tend to restrict unhealthy food (OR: 1.37; 95% CI: 1.03, 1.82) and girls whose mothers taught them about nutrition (OR: 2.09; 95% CI: 1.19, 3.67) were more likely to have high food reward. No further significant associations were observed between food reward, other appetitive traits and feeding practices. Despite the lack of association with child weight status, this study demonstrated the value of the write-for-food task to assess food reward in children and presented sex-specific associations with maternal feeding practices.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".