Ecologic Proxies for Household Income How Well Do They Work for the Analysis of Health and
Bibliographic record
Abstract
Background: Researchers often use census-derived measures of socioeconomic status (SES) when personal information is not available. Theory predicts that the resulting misclassification will blunt associations between outcomes and SES and that control for confounding by SES will be less effective. The purpose of this paper was to examine the magnitude of this problem using data from the National Population Health Survey (NPHS). Methods: Subjects were 4,037 respondents to the NPHS who were linked to the Ontario Health Insurance Plan. An ecologic measure of income was obtained by linkage of subjects ’ postal codes to the Census. Results: The relationships between the ecologic-level measure and health outcomes or health services utilization were attenuated in comparison to the relationships relative to the direct measure of household income. The ecologic measure also produced poorer control for confounding by income in the analysis of other health relationships. Conclusions: Many interesting public health and health services questions can be addressed only with the use of ecologic level socioeconomic information. While most of
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".