Examining Health-Seeking Behavior among Diverse Ethnic Subgroups within Black Populations in the United States and Canada: A Cross-Sectional Study
Bibliographic record
Abstract
The Black populations, often treated as ethnically homogenous, face a constant challenge in accessing and utilizing healthcare services. This study examines the intra-group differences in health-seeking behavior among diverse ethnic subgroups within Black communities. A cross-sectional analysis included 239 adults ≥18 years of age who self-identified as Black in the United States and Canada. Multiple logistic regression assessed the relationship between health-seeking behaviors and ethnic origin, controlling for selected social and health-related factors. The mean age of the participants was 38.6 years, 31% were male, and 20% were unemployed. Sixty-one percent reported a very good or excellent health status, and 59.7% were not receiving treatment for chronic conditions. Advancing age (OR = 1.05, CI: 1.01-1.09), female gender (OR = 3.09, CI: 1.47-6.47), and unemployment (OR = 3.46, CI: 1.35-8.90) were associated with favorable health-seeking behaviors. Compared with the participants with graduate degrees, individuals with high school diplomas or less (OR = 3.80, CI: 1.07-13.4) and bachelor's degrees (OR = 3.57, CI: 1.3-9.23) were more inclined to have engaged in favorable health-seeking behavior compared to those with graduate degrees. Across the Black communities in our sample, irrespective of ethnic origins or country of birth, determinants of health-seeking behavior were age, gender, employment status, and educational attainment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".