Immigrants, Ethnicity, and Adherence to Secondary Cardiac Prevention Therapy: A Substudy of the ISLAND Trial
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to evaluate adherence to guideline-recommended cardiac secondary prevention therapies by immigration and ethnicity. METHODS: ecreasing Cardiovascular Events (ISLAND) randomized controlled trial. A cohort of 1642 participants was analyzed. Patients were categorized based on their self-reported immigrant status as being Canadian or foreign born and based on their visual minority status (as European or a visual minority). We used logistic regression to examine associations between these patient characteristics of interest and patient adherence to statin medication 1 year after myocardial infarction (MI) and completion of cardiac rehabilitation, adjusting for age, sex, and comorbidities. RESULTS: The dataset included outcome data on 1049 (64%) Canadian-born patients and 593 (36%) immigrants. There were 347 (21%) who identified as a visual minority. We report a nonsignificant trend in statin adherence 1 year after MI favouring foreign-born participants compared with Canadian-born participants (odds ratio [OR], 1.26; 95% confidence interval [CI], 0.91-1.68). Visual minorities were found to have no significant difference in statin adherence 1 year after MI compared with participants of European ethnicity (OR, 1.04; 95% CI, 0.72-1.51). Neither immigration status (OR, 0.91; 95% CI, 0.72-1.15) nor visual minority status (OR, 0.97; 95% CI, 0.73-1.28) were associated with cardiac rehabilitation completion. CONCLUSIONS: Our findings offer limited support that immigrants with > 10 years of Canadian residency exposure experience greater adherence to statins 1 year after MI. Further research is required to better inform our understanding of secondary prevention strategy among immigrant populations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".