Strategies for health education in North American immigrant populations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: This article is intended to stimulate critical thinking about barriers and strategies related to health education for immigrant populations. Its rationale is to promote an understanding and appreciation for the individuality and diversity of immigrant beliefs, values and culture, and how these contribute to health education through nursing practice, research and theory. BACKGROUND: Since 2005, over 1,250,000 immigrants annually have obtained legal permanent residence in North America [over 1 million annually in the United States of America (USA) and over 250,000 annually in Canada]. THE PROBLEM: While a broad immigration policy leads to population growth, cultural change and ethnic diversity, migration impacts immigrants' health status. In North America, the 'healthy immigrant effect', whereby immigrants generally tend to be healthier than individuals born in host countries, steadily declines after immigration. METHODS: Immigration statistics and reports on literacy and learning were sourced from official websites in Canada and the USA. These were reviewed and discussed in the context of scholarly published literature on health literacy, health education and health promotion. OPPORTUNITIES: Promoting health in immigrant populations is difficult due to cultural, linguistic, health literacy and socio-economic barriers. Cultural sensitivity, careful inquiry and comprehensive knowledge of immigrants' social circumstances are essential to every health education programme. CONCLUSION: Strategies for immigrant health education must be technologically diverse, involve partnerships with multidisciplinary professionals, elicit active community participation, and facilitate language transfer and interpretation. Future research must continue to explore these barriers and strategies, using both qualitative and quantitative methodologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it