Patient perspectives on routinely being asked about their race and ethnicity: Qualitative study in primary care.
Bibliographic record
Abstract
OBJECTIVE: To understand patients' perspectives on responding to a question about their race and ethnicity in a primary care setting. DESIGN: Qualitative study using semistructured individual interviews conducted between May and July 2016. SETTING: An academic family health team in Toronto, Ont, where collection of sociodemographic data has been routine since 2013. PARTICIPANTS: Twenty-seven patients from 5 of the 6 clinic sites of the family health team, ranging in age, sex, educational background, and immigration status. METHODS: , and what response options they considered. Interviews were audiorecorded, transcribed, and coded iteratively. MAIN FINDINGS: Patients did not report discomfort with responding to a question about race and ethnicity in their family doctor's office. Although many patients considered the question straightforward, some patients reported different interpretations of the question. For example, some thought the question about race and ethnicity related to parental origin or ancestry, whereas others considered the question to be about personal place of birth or upbringing. Many patients appreciated being able to select from a variety of specific response options, but this also posed a difficulty for patients who could not easily find an option that reflected their identity. Patients with mixed heritage experienced the most challenges selecting a response. CONCLUSION: Patients attending a primary care clinic were not uncomfortable responding to a question about race and ethnicity. However, patients had different interpretations of what was being asked. Future research should explore perspectives of patients in other primary care settings and test different methods for collecting data about their race and ethnicity.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".