Sociodemographic Data Categorization and Health Equity Research: Expressions of Racial and Ethnic Identity in the Giving Voice to Mothers Study
Bibliographic record
Abstract
Background: The proportion of the U.S. population identifying with multiple races and ethnicities has increased in the last decade, but we have limited knowledge of how these individuals self-identity. Methods: We conducted a secondary analysis of data on how participants reported their racial/ethnic identities in Giving Voices to Mothers (GVtM), a community-based participatory research study (2016–2017) capturing the perspectives of childbearing people from communities of color and those who planned births at home or birth centers in the United States. Survey items were codeveloped by service users and community health workers. We used descriptive and bivariate statistics to explore how respondents reported racial/ethnic identity, how multiracial identity was related to personal characteristics, and how people used the “other” category. Results: Of 2700 survey participants, 2522 (93%) responded to the race/ethnicity questions. Respondents who expressed multiracial identity ( n = 339) most often marked more than one racial/ethnic category (78%) or marked the category “biracial” (22%). Multiracial respondents were more likely to be 29 years or younger, to live in the Southern or Western regions of the United States, and to be of low socioeconomic status. In contrast, individuals identifying with the specific term “biracial” were more likely to live in the Midwest or Northeast and to have a higher socioeconomic status. Conclusion: The GVtM model for sociodemographic data collection demonstrates how community members can inform the design of racial/ethnic categories that better reflect their lived experience and preferences for self-identification. This can, in turn, enhance participation in and value of findings on health inequities.
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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 | 0.031 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".