Segregated by wealth, health, and development : an analysis of pre-school child health in a medium-sized German city
Bibliographic record
Abstract
The School Entry Examination (SEE) can be used to identify children with current health issues, developmental delays, and risk factors for later diseases. This study analyzes the health status of preschool children in a German city with considerable socio-economic differences among its quarters. We used secondary data from SEEs 2016–2019 from the entire city (8417 children), which we divided into quarters with low (LSEB), medium (MSEB), and high socioeconomic burden (HSEB). In HSEB quarters, 11.3% of children were overweight as opposed to 5.3% in LSEB quarters. In HSEB quarters, 17.2% of children had sub-par cognitive development in contrast to 1.5% in LSEB quarters. For overall sub-par development, LSEB quarters had a prevalence of 3.3%, whereas, in HSEB quarters, 35.8% of children received this result. Logistic regression was used to determine the influence of the city quarter on the outcome of overall sub-par development. Here, considerable disparities among HSEB and LSEB quarters remained after adjustment for parents’ employment status and education. Pre-school children in HSEB quarters showed a higher risk for later disease than children in LSEB quarters. The city quarter had an association with child health and development that should be considered in the formulation of interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".