Evaluating the accuracy of a geographic closed-ended approach to ethnicity measurement, a practical alternative
Bibliographic record
Abstract
Purpose: Measuring ethnicity accurately is important for identifying ethnicity variations in disease risk. We evaluated the degree of agreement and accuracy of maternal ethnicity measured using the new standardized closed-ended geographically based ethnicity question and geographic reclassification of open-ended ethnicity questions from the Canadian census. Methods: A prospectively designed study of respondent agreement of mothers of healthy children age 1-5 years recruited through the TARGet Kids! practice based research network. For the primary analysis, the degree of agreement between geographic reclassification of the Canadian census maternal ethnicity variables and the new geographically based closed-ended maternal ethnicity variable completed by the same respondent was evaluated using a kappa analysis. Results: 862 mothers who completed both measures of ethnicity were included in the analysis. The kappa agreement statistic for the two definitions of maternal ethnicity was 0.87 (95% CI: 0.84-0.90) indicating good agreement. Overall accuracy of the measurement was 93%. Sensitivity and specificity ranged from 83-100% and 96-100% respectively. Conclusion: The new standardized closed-ended geographically based ethnicity question represents a practical alternative to widely used open-ended ethnicity questions. It may reduce risk of misinterpretation of ethnicity by respondents, simplify analysis and improve the accuracy of ethnicity measurement.
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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 | 0.253 | 0.477 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".