Immigration, generational status and health literacy in Canada
Bibliographic record
Abstract
Background: Immigrants, a fast-growing population in Canada, score below the national average in health literacy, but the reasons behind the low scores are largely unknown. Also, there is a need to understand the long-term impact of immigration by examining health literacy by generational status. Objective: To examine health literacy differentials in Canada, comparing immigrants and non-immigrants; as well as immigrant sub-groups by their region of origin, recency of arrival and generational status. Methods: A cross-sectional multi-stage representative sample of 22,818 respondents from the Canadian component of the 2003 International Adult Literacy and Skills survey. Data were collected in the home of each respondent. We used descriptive statistics and logistic regression in this study. Results: Fewer immigrants (24%) than non-immigrants (44%) had the requisite health literacy level. After controlling for selected factors, health literacy was still significantly lower for immigrants compared to non-immigrants (OR = 0.51; 95% CI = 0.37 – 0.70). For the immigrant sub-groups and by generations, initial differences that were observed, disappeared after controlling for selected factors. Overall, health literacy was associated with economic, education, and literacy-related factors, but the association with literacy practices at home and at work was stronger for the immigrants than the non-immigrants. Conclusion: Education and literacy practices at home and at work are important determinants of the population’s health literacy; however, education is more likely to be associated with acquisition, while literacy practices are more likely associated with maintenance of health literacy. This adds to our understanding of the factors associated with health literacy, by immigrant and generational status, and shows how different segments of the population in Canada process health information.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| 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 | 0.000 |
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".