Approaches to Community Engagement That Optimize the Reach and Utility of Health Education Campaigns in Newcomer Communities
Bibliographic record
Abstract
Linguistic and cultural factors are significant barriers to health education for newcomers, defined for this report as people who recently arrived to the United States as refugees, asylees, immigrants, migrants, and others in need of international protection. However, many newcomers are in a good position to influence health education strategies. The National Resource Center for Refugees, Immigrants and Migrants (NRC-RIM) developed campaigns using both community-informed and community co-design approaches in order to optimize their reach and utility. A community-informed approach allows organizations to create linguistically and culturally relevant health education materials relatively quickly on a large scale to meet communities' needs. The six steps included (1) Listen, (2) Write, (3) Design, (4) Translate, (5) Validate, and (6) Scale. A community co-design approach leverages the wisdom and experience of community leaders to create hyperlocal campaigns that are rooted in community values. The three steps included (1) Inspiration, (2) Ideation, and (3) Implementation. A mixed-methods evaluation showed a complementary approach to be effective in promoting informed decision-making and health-seeking behavior among newcomers. The findings underscore the crucial need for culturally relevant communications created in genuine partnership with communities, and suggest that by investing time and resources to this process, organizations can be well-positioned to address health inequities among newcomers.
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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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".