Bibliographic record
Abstract
Study objectives: To determine which area based socioeconomic measures can meaningfully be used, at which level of geography, to monitor socioeconomic inequalities in childhood health in the US. Design: Cross sectional analysis of birth certificate and childhood lead poisoning registry data, geocoded and linked to diverse area based socioeconomic measures that were generated at three geographical levels: census tract, block group, and ZIP code. Setting: Two US states: Massachusetts (1990 population=6 016 425) and Rhode Island (1990 population=1 003 464). Participants: All births born to mothers ages 15 to 55 years old who were residents of either Massachusetts (1989–1991; n=267 311) or Rhode Island (1987–1993; n=96 138), and all children ages 1 to 5 years residing in Rhode Island who were screened for lead levels between 1994 and 1996 (n=62 514 children, restricted to first test during the study period). Main results: Analyses of both the birth weight and lead data indicated that: (a) block group and tract socioeconomic measures performed similarly within and across both states, while ZIP code level measures tended to detect smaller effects; (b) measures pertaining to economic poverty detected stronger gradients than measures of education, occupation, and wealth; (c) results were similar for categories generated by quintiles and by a priori categorical cut off points; and (d) the area based socioeconomic measures yielded estimates of effect equal to or augmenting those detected, respectively, by individual level educational data for birth outcomes and by the area based housing measure recommended by the US government for monitoring childhood lead poisoning. Conclusions: Census tract or block group area based socioeconomic measures of economic deprivation could be meaningfully used in conjunction with US public health surveillance systems to enable or enhance monitoring of social inequalities in health in the United States.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".