Adding Natural Areas to Social Indicators of Intra-Urban Health Inequalities among Children: A Case Study from Berlin, Germany
Bibliographic record
Abstract
Research suggests that there is a relationship between the health of urban populations and the availability of green and water spaces in their daily environment. In this paper, we analyze the potential intra-urban relationships between children's health determinants and outcomes and natural areas in Berlin, Germany. In particular, health indicators such as deficits in viso-motoric development in children are related to environmental indicators such as the natural area cover, natural area per capita and distance to natural areas; however, these indicators are also correlated with social determinants of health. The methodological approach used in this study included bivariate and multivariate analyses to explore the relations between health inequalities and social, socio-economic, and land use parameters. The results on a sub-district level indicated that there was a correlation between natural areas and social health determinants, both of which displayed a certain intra-urban spatial pattern. In particular, a lower percentage of natural area cover was correlated with deficits in viso-motoric development. However, results with percentage of natural area cover and per capita natural area with childhood overweight were not conclusive. No significant correlation was found for percentage of natural area cover and overweight, while significant negative correlation values were found between overweight and per capita natural area. This was identified particularly in the districts that had lower social conditions. On the other hand, the districts with the highest social conditions had the comparatively lowest levels of complete measles immunization. This study may facilitate public health work by identifying the urban areas in which the strengthening of health resources and actions should be prioritized and also calls for the inclusion of natural areas among the social health indicators included in intra-urban health inequality tools.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".