Evaluating a Culturally Tailored Public Health Forum in Improving the Knowledge and Understanding of Chronic Disease Management in the Chinese Population: A Mixed-methods Study
Bibliographic record
Abstract
Background: Chronic disease management is fraught with many challenges for ethnic minorities. Studies conducted in non-multicultural populations suggest that patient and community engaging initiatives can improve chronic disease management practices. However, literature on culturally specific community engaging programs is relatively sparse. The interCultural Online Health Network (iCON) is a culturally tailored, patient and community engaging health promotion program, which provides culturally specific health education to BC’s multicultural communities. We aimed to assess if the iCON 2020 Chinese Health Forum can improve the knowledge and understanding of chronic disease self-management in the Chinese community of Vancouver, BC. Methods: We conducted a sequential mixed-methods study by administering pre- and post- validated questionnaires, followed by semi-structured interviews conducted one-two months after the forum. We assessed our primary outcome of difference in self-efficacy scores post-forum using paired t-tests and further illuminated our research question through a thematic analysis of the semi-structured interviews. Results: From the 381 participants that attended the Health Forum, 131 consented to completing the pre- and/or post- surveys, and seven provided consent to participate in the follow-up interview. There was a statistically significant difference in self-efficacy scores pre- and post- forum participation (Mean difference = 0.58, S.D. = 1.42; [95% CI: 0.26 – 0.90], t(77) = 3.60; P = 0.001, d = 0.41). Participants attributed the effectiveness of the Health Forum to its accessible yet engaging programming and focus on culturally tailored health education. Conclusion: A culturally tailored, patient engagement and community outreach program effectively improved Chinese community members self-efficacy in managing their chronic diseases and was well received by participants. iCON’s 2020 Chinese Health Forum presents a model with associated principles of approach for similar culturally specific health education and community engagement programs that need to be developed to reduce the burden of chronic diseases in multicultural 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 | 0.030 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".