MétaCan
Menu
Back to cohort
Record W3124827255 · doi:10.1002/hec.1732

Parental income and child health in Germany

2011· article· en· W3124827255 on OpenAlexaboutno aff
Steffen Reinhold, Hendrik Jürges

Bibliographic record

VenueHealth Economics · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
FundersScience Foundation Ireland
KeywordsDemographic economicsChild healthPsychologyEconomicsMedicinePediatrics

Abstract

fetched live from OpenAlex

Using newly available data from Germany, we study the relationship between parental income and child health. We find a strong gradient between parental income and subjective child health as has been documented earlier in the United States, Canada, and the United Kingdom. The relationship in Germany is about as strong as in the United States and stronger than in the United Kingdom. However, in contrast to US results, we do not find consistent evidence that the disadvantages associated with low parental income accumulate as the child ages, nor that children from low socioeconomic background are more likely to suffer from doctor-diagnosed conditions. There is some evidence, however, that high-income children are better able to cope with the adverse consequences of chronic conditions. Investigating potential diagnosis bias, we find only weak evidence for health disadvantages for low-income children when using objective health measures, but some evidence for under-utilization of health services among low-income families.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.049
GPT teacher head0.333
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations97
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueHealth EconomicsSame topicHealth disparities and outcomesFrench-language works237,207