Agreement in measuring socio-economic status: area-based versus individual measures.
Bibliographic record
Abstract
Area-based socio-economic status (SES) measures are frequently used in epidemiology. Such an approach assumes socio-economic homogeneity within an area. To quantify the agreement between area-based SES measures and SES assessed at the individual level, we conducted a cross-sectional study of 943 children who resided in 155 small enumeration areas and 117 census tracts from 18 schools in Montreal, Quebec. We used street address information together with 1986 census data and parental occupation to establish area-based and individual level SES indicators, respectively. As compared with the SES score determined at the level of the individual, 13 different area-based SES indices classified the children within the same quintile 28.7% (+/- 2.8%) of the time. The discrepancy was within one quintile in 35.3% (+/- 2.3%) of cases, two quintiles in 20.6% (+/- 3.6%), three quintiles in 11.3% (+/- 4.2%) and four quintiles in 4.1% (+/- 0.2%). In conclusion, we observed a substantial discrepancy between area- based SES measures and SES assessed at the individual level. Caution should therefore be used in designing or interpreting the results of studies in which area-based SES measures are used to test hypotheses or control for confounding.
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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 | 0.058 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".