Area-level socioeconomic disparity trends in nutritional status among 5–6-year-old children in Israel
Bibliographic record
Abstract
Objective This study aimed to assess area-level socioeconomic position (SEP) disparities in nutritional status, to determine whether disparities differed by sex and to assess whether nutritional status and disparities changed over time. Design We used repeated cross-sectional data from a national programme that evaluates the quality of healthcare in Israel to assess children’s nutritional status. Setting The study included all Israeli residents aged 7 years during 2014–2018 (n=699 255). Methods SEP was measured based on the Central Bureau of Statistics’ statistical areas, and grouped into categories, ranging from 1 (lowest) to 10 (highest). We used multivariable multinomial regression to assess the association between SEP and nutritional status and between year and nutritional status. We included interactions between year and SEP to assess whether disparities changed over time. Results Children in SEP 1, comprised entirely of children from the Bedouin population from Southern Israel, had drastically higher odds of thinness compared with those in the highest SEP (Girls: OR 5.02, 99% CI 2.23 to 11.30; Boys: OR 2.03, 99% CI 1.19 to 3.48). Odds of obesity were highest in lower-middle SEPs (ORSEP 5 vs 10 1.84, 99% CI 1.34 to 2.54). Prevalence of overweight and obesity decreased between 2014 and 2018, normal weight increased and thinness did not change. SEP disparities in thinness decreased over time in boys but showed a reverse trend for girls. No substantial improvement was seen in SEP disparities for other weight categories. Conclusions Our study demonstrates the need to consider initiatives to combat the considerable SEP disparities in both thinness and obesity.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".