Why Collect and Use Race/Ethnicity Data? A Qualitative Case Study on the Perspectives of Mental Health Providers and Patients During COVID-19
Bibliographic record
Abstract
CONTEXT: Calls to collect patients' race/ethnicity (RE) data as a measure to promote equitable health care among vulnerable patient groups are increasing. The COVID-19 pandemic has highlighted how a public health crisis disproportionately affects racialized patient groups. However, less is known about the uptake of RE data collection in the context of mental health care services. METHODOLOGY: A qualitative case study used surveys with mental health patients (n = 47) and providers (n = 12), a retrospective chart review, and a focus group to explore healthcare providers' and patients' perspectives on collecting RE data in Canada. RESULTS: The patient survey data and focus groups show that patients avoid providing identifying information due to perceived stigma and discrimination and a lack of trust. Providers did not feel comfortable asking patients about RE, leading to chart review data where RE information was not systematically collected. CONCLUSIONS: The uptake and implementation of RE data collection in mental health care contexts require increased training and support, systematic implementation, and further evaluation and measurement of how the collection of RE data will be used to mitigate systemic racism and improve mental health outcomes.
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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 | 0.032 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".