Agreement between birthplace and self-reported ethnicity in a population-based mammography service.
Bibliographic record
Abstract
BACKGROUND: Ethnicity is associated with genetic, environmental, lifestyle and social constructs. Difficult to define using a single variable, but strongly predictive of health outcomes and useful for planning healthcare services, it is often lacking in administrative databases, necessitating the use of a surrogate measure. A potential surrogate for ethnicity is birthplace. Our aim was to measure the agreement between birthplace and ethnicity among six major ethic groups as recorded at the population-based mammography service for British Columbia, Canada (BC). METHODS: We used records from the most-recent visits of women attending the Screening Mammography Program of British Columbia to cross-tabulate women's birthplaces and self-reported ethnicities, and separately considered results for the time periods 1990-1999 and 2000-2006. In general, we combined countries according to the system adopted by the United Nations, and defined ethnic groups that correspond to the nation groups. The analysis considered birthplaces and corresponding ethnicities for South Asia, East/Southeast Asia, North Europe, South Europe, East Europe, West Europe and all other nations combined. We used the kappa statistic to measure the concordance between self-reported ethnicity and birthplace. RESULTS: Except for the 'Other' category, the most-common birthplace was East/Southeast Asia and the most-common ethnicity was East/Southeast Asian. The agreement between birthplace and self-reported ethnicity was poor overall, as evidenced by kappa scores of 0.22 in both 1990-1999 and 2000-2006. There was substantial agreement between ethnicity and birthplace for South Asians, excellent agreement for East/Southeast Asians, but poor agreement for Europeans. CONCLUSION: Birthplace can be used as a surrogate for ethnicity amongst people with South Asian and East/Southeast Asian ethnicity in BC.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".