Can ethnicity data collected at an organizational level be useful in addressing health and healthcare inequities?
Bibliographic record
Abstract
OBJECTIVE: Following arguments made in the USA, the UK and New Zealand regarding the importance of population-level ethnicity data in understanding health and healthcare inequities, health authorities in several Canadian provinces are considering plans to collect ethnicity data from patients at the point of care within selected healthcare organizations. The purpose of this paper is to examine the potential quality, utility and relevance of ethnicity data collected at an organizational level as a means of addressing health and healthcare inequities. DESIGN: We draw on findings from a recent Canadian study that examined the implications of collecting ethnicity data in healthcare contexts. Using a qualitative design, data were collected in a large city, and included interviews with 104 patients, community and healthcare leaders, and healthcare workers within diverse clinical contexts. Data were analyzed using interpretive thematic analysis. RESULTS: Our results are discussed in relation to discourses reflected in the current literature that require consideration in relation to the potential utility and relevancy of ethnicity data collected at the point of care within healthcare organizations. These discourses frame excerpts from the ethnographic data that are used as illustrative examples. Three key challenges to the potential relevance and utility of ethnicity data collected at the level of local healthcare organizations are identified: (a) issues pertaining to quality of the data, (b) the fact that data quality is most problematic for those with the greatest vulnerability to the negative effects of health inequities, and (c) the lack of data reflecting structural disadvantages or discrimination. CONCLUSION: The quality of ethnicity data collected within healthcare organizations is often unreliable, particularly for people from racialized or visible minority groups, who are most at risk, seriously limiting the usefulness of the data. Quality measures for collecting data reflecting ethnocultural identity in specific healthcare organizations may be warranted - but only if mechanisms exist or are developed for linking ethnicity with measures of perceived discrimination, stigmatization, income level, and other known contributors to inequities. Methods for linking these kinds of data, however, remain underdeveloped or non-existent in most healthcare organizations.
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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 | 0.171 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".