Public health community engagement with Asian populations in British Columbia during COVID-19: towards a culture-centered approach
Bibliographic record
Abstract
OBJECTIVES: COVID-19 has posed significant challenges to those who endeavour to provide equitable public health information and services. We examine how community leaders, advocates, and public health communication specialists have approached community engagement among Asian immigrant and diaspora communities in British Columbia throughout the pandemic. METHODS: Qualitative interviews with 27 participants working with Asian communities in a healthcare, community service, or public health setting, inductively coded and analyzed following the culture-centred approach to health communication, which focuses on intersections of structure, culture, and agency. RESULTS: Participants detailed outreach efforts aimed at those who might not be reached by conventional public health communication strategies. Pre-existing structural barriers such as poverty, racial disparities, and inequitable employment conditions were cited as complicating Asian diaspora communities' experience of the pandemic. Such disparities exacerbated the challenges of language barriers, information overload, and rapidly shifting recommendations. Participants suggested building capacity within existing community service and public health outreach infrastructures, which were understood to be too lean to meet community needs, particularly in a pandemic setting. CONCLUSION: A greater emphasis on collaboration is key to the provision of health services and information for these demographic groups. Setting priorities according to community need, in direct collaboration with community representatives, and further integrating pre-existing bonds of trust within communities into public health communication and engagement strategies would facilitate the provision of more equitable health information and services. This mode of engagement forgoes the conventional focus on individual behaviour change, and focuses instead on fostering community connections. Such an approach harmonizes with community support work, strengthening the capacity of community members to secure health during public health emergencies.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| 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".