Expressed racial identity and hypertension in a telephone survey sample from Toronto and Vancouver, Canada: do socioeconomic status, perceived discrimination and psychosocial stress explain the relatively high risk of hypertension for Black Canadians?
Bibliographic record
Abstract
INTRODUCTION: Canadian research on racial health inequalities that foregrounds socially constructed racial identities and social factors which can explain consequent racial health inequalities is rare. This paper adopts a social typology of salient racial identities in contemporary Canada, empirically documents consequent racial inequalities in hypertension in an original survey dataset from Toronto and Vancouver, Canada, and then attempts to explain the inequalities in hypertension with information on socioeconomic status, perceived experiences with institutionalized and interpersonal discrimination, and psychosocial stress. METHODS: Telephone interviews were conducted in 2009 with 706 randomly selected adults living in the City of Toronto and 838 randomly selected adults living in the Vancouver Census Metropolitan Area. Bivariate analyses and logistic regression modeling were used to examine relationships between racial identity, hypertension, socio-demographic factors, socioeconomic status, perceived discrimination and psychosocial stress. RESULTS: The Black Canadians in the sample were the most likely to report major and routine discriminatory experiences and were the least educated and the poorest. Black respondents were significantly more likely than Asian, South Asian and White respondents to report hypertension controlling for age, immigrant status and city of residence. Of the explanatory factors examined in this study, only educational attainment explained some of the relative risk of hypertension for Black respondents. Most of the risk remained unexplained in the models. CONCLUSIONS: Consistent with previous Canadian research, socioeconomic status explained a small portion of the relatively high risk of hypertension documented for the Black respondents. Perceived experiences of discrimination both major and routine and self-reported psychosocial stress did not explain these racial inequalities in hypertension. Conducting subgroup analyses by gender, discerning between real and perceived experiences of discrimination and considering potentially moderating factors such as coping strategy and internalization of racial stereotypes are important issues to address in future Canadian racial inequalities research of this kind.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".