Building Healthcare Navigation Skills Through a Health Literacy Program for Immigrants in Rural British Columbia
Bibliographic record
Abstract
Background: Canada welcomes over 300,000 immigrants each year with plans to increase this to 500,000 immigrants by 2025. Newcomers to Canada will require health services to maintain their health and well-being. There is a scarcity of existing research on programs that address the barriers immigrants face when accessing health services in small urban and rural communities and what potential interventions may address those barriers. Objectives: A mixed-methods study was conducted in two phases. The purpose of Phase 1 was to discover the health information needs of immigrants living in small urban and rural communities in the southern interior of British Columbia and to develop a workshop to help address those needs. Phase 2 aimed to deliver and evaluate the impact of The Staying Healthy Workshop Series, a health literacy (HL) program to build knowledge in navigating and accessing health services. Methods: In Phase 1, through convenience sampling, service providers and immigrant clients were recruited to contribute to the development of a HL program by sharing what was important to them when it comes to healthcare. A qualitative descriptive approach was used to analyze data collected. In Phase 2, a pilot of the HL program was delivered and evaluated with 16 participants through an English-language class. The reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) framework was used to design a mixed-methods evaluation of the program. Results: The program was promoted to clients of South Okanagan Immigrant and Community Services in British Columbia. Participants rated the modules of the series positively for understanding and satisfaction, with the mental health module being rated highest. Successful implementation of the program required organization-level support from facilitators and leadership. The early involvement of clients and service providers was an enabler for implementation. Scale-up and sustainability would be potential future opportunities to explore. Conclusion: Partnerships with community organizations to deliver HL programs introducing Canadian healthcare to immigrants living in small urban and rural communities in BC is a valuable approach to enhancing access to health services, particularly for those learning English as a second language.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".