Diabetes and the Hispanic Health Paradox: insights from Hispanics in Canada
Bibliographic record
Abstract
OBJECTIVES: The Hispanic Health Paradox suggests that Hispanics and their culture may possess certain protective factors that mitigate the negative impact of lower socioeconomic status on health. Much of the existing literature has focused on the United States. Such paradoxical advantage on diabetes was explored among Hispanics in Canada. DESIGN: Secondary data from four cycles of the Canadian Community Health Survey from 2015 to 2018 were examined. Multivariate logistic regression analyses were conducted with the following samples: Hispanics (1,799), Non-Hispanic White (168,225), and other racialized groups (33,730). The statistical and practical significance or strength and precision of the predictor-outcome relationships were estimated with odds ratios (OR) and their 95% confidence intervals (CIs) that were derived from regression statistics. RESULTS: Despite overall lower socioeconomic status, Hispanics were about 79% less likely than Non-Hispanic Whites to have diabetes. Hispanic ethnicity significantly interacted with age, sex, income, and immigration status in predicting diabetes risk. Hispanic ethnicity was most protective for middle-aged adults (OR = 0.72) but not seniors. Hispanic males experienced greater protection (OR = 0.77) than females (OR = 0.90). Low-income Hispanics showed the strongest protective effects (ORs = 0.62-0.85). Recent immigrants to Canada (<10 years) exhibited moderate protection (ORs = 0.90-0.93), though unexpectedly, Canadian-born Hispanics had the lowest risk (OR = 0.59). CONCLUSIONS: These findings highlight the nuanced and paradoxical protective effects of Hispanic ethnicity on diabetes risk.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".