Health literacy among members of the Nepalese immigrant population in Canada
Bibliographic record
Abstract
Background: Health literacy is an important public health concern and can be defined as ‘the degree or extent to which the individuals have the capacity to obtain, process and understand basic health information and services to make appropriate health decisions’. Research on health literacy among recent immigrants to Canada is not that extensive. Objective: The purpose of this paper was to describe health literacy status among Nepalese immigrants residing in Calgary, Alberta, Canada. Methods: In 2019, a cross-sectional study, using a self-administered questionnaire, was conducted among Nepalese immigrants in Calgary. The questionnaire comprised 38 questions including sociodemographic information, self-rated health status, having a chronic disease or not, health literacy, sources of health information and preference to gain health information. Results: We received 401 responses: 49.63% were from women, 51.37% were aged 36–55 years, 37.00% had graduate-level education, 44.96% had immigrated to Canada less than 5 years ago and 81.05% were employed full-time/part-time or self-employed. Findings revealed that 17.21% of survey participants had limited health literacy, followed by 40.15% who had marginal health literacy. The majority of the survey participants (71.82%) either always or often got health information from healthcare professionals, followed by online resources (56.61%). Conclusion: Noteworthy levels of limited health literacy and marginal health literacy were observed among the Nepalese immigrant population. Multidirectional, culturally tailored, community-led, collaborative initiatives are needed to improve health literacy among the immigrant population, to lessen health disparities and to promote better health outcomes.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".