Feeding neophobia and current feeding problems – a cross-sectional study among Polish children aged 2–7 years
Bibliographic record
Abstract
Introduction The main aim of the study was to identify the prevalence of food neophobia using the standardised food neophobia of children scale (FNSC) questionnaire in a group of Polish children attending nurseries and kindergartens. Material and methods The study was carried out using a survey method. The questionnaire was distributed to randomly selected nurseries and kindergartens. The period in which we conducted the above survey was January – March 2023. A total of 585 pairs of mothers and their children participated in the survey. A standardised questionnaire assessing food neophobia among children was used to assess food neophobia FNSC. A score below 27 indicated a low risk of neophobia, 28–40 an intermediate risk, and a score above 41 was a high risk. Results In the study group, 171 children (29.23%) had a low risk of food neophobia, 182 children (31.11%) had a medium risk, and 232 children (39.66%) had a high risk. There were no differences in the risk of food neophobia between girls and boys (p = 0.907), between children’s weight (p = 0.776), or between place of residence (p = 0.095). There was a statistically significant difference between age and in the risk of food neophobia (p = 0.0002). Conclusions In the study group, 40% of the children had a high risk of food neophobia. Food neophobia was highest among 4-year-olds and 5-year-olds. There were no differences between girls and boys and the prevalence of food neophobia. Among children with a higher risk of food neophobia, feeding problems such as playing while eating meals, fussing at meals, and picky eating were more common.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".