Ethnicity and health literacy: a survey on hypertension knowledge among Canadian ethnic populations.
Bibliographic record
Abstract
OBJECTIVES: With an increase and diversity in ethnic populations in Westernized countries, understanding the differences in levels of knowledge surrounding hypertension is important in planning appropriate prevention strategies. The purpose of our study was to assess levels of hypertension knowledge in Chinese, Indian and White populations in a large metropolitan Canadian city. DESIGN: A telephone survey was conducted in English, Chinese (Cantonese and Mandarin) and Indian languages (Hindi, Punjabi and Urdu). Hypertension knowledge was assessed through a 10-item validated instrument; respondents received 1 point for each correct answer. Logistic regression was used to test differences in hypertension knowledge among these three populations. RESULTS: Survey response rates were 68.7% (301) for Chinese, 61.3% (248) for Indian and 69.7% (254) for White populations. The average hypertension knowledge score for Chinese respondents was 7.23 out of 10, 7.11 for Indian respondents and 7.28 for White respondents. Compared to White respondents, Chinese respondents were less likely than White respondents to know high blood pressure can cause heart attacks (adjusted odds ratio [aOR]: .43, 95% confidence interval [CI]: .19-.96] and Indian respondents were less likely to know losing weight usually decreases blood pressure (aOR: .38, 95% CI: .21-.68). CONCLUSIONS: Hypertension knowledge levels among these three ethnic/racial populations were similar and relatively high and varied by content. Low levels of knowledge for Chinese and Indian ethnic populations were on hypertension risk factors, long-term consequences of hypertension and anti-hypertensive medication adherence. Specifically, females, recent immigrants to Canada and Chinese seniors were identified as sub-groups who should be targeted for hypertension knowledge promotion.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".