Demographic and Occupational Differences Between Ethnic Minority Workers Who Did and Did Not Complete the Telephone Survey in English
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Limited research indicates that using English language only surveys in prevalence studies conducted in the general population or in specific ethnic populations may result in unrepresentative samples and biased results. In this study, we investigated whether participants from ethnic minorities who chose to answer a study interview in a language other than English (LOTE) differed from those who completed the interview in English. METHODS: This study was conducted within an Australian population-based telephone survey that assessed the prevalence of occupational exposure to carcinogens among 749 ethnic minority workers. We used modified Poisson regression to determine the factors associated with completing the interview in a LOTE. RESULTS: Participants who elected to complete the interview in a LOTE differed from those who completed it in English on several factors, including sex, country of birth, education, occupation, and occupational exposure to carcinogens (40% compared with 29%, P < 0.01). CONCLUSIONS: The participants who chose to complete the study interview in their native language had several demographic differences from those participants who completed it in English, and were more likely to be exposed to carcinogens at work. Prevalence studies that offer only English language study instruments are unlikely to produce representative samples of minority groups, and may therefore produce biased results.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".