Partnership for Healthier Asians: Disseminating Evidence-Based Practices in Asian-American Communities Using a Market-Oriented and Multilevel Approach
Bibliographic record
Abstract
BACKGROUND: One of the greatest challenges facing health promotion and disease prevention is translating research findings into evidence-based practices (EBP). There is currently a limited research base to inform the design of dissemination action plans, especially within medically underserved communities. OBJECTIVE: The objective of this paper is to describe an innovative study protocol to disseminate colorectal cancer (CRC) screening guidelines in seven Asian subgroups. METHODS: This study integrated a market-oriented Push-Pull-Infrastructure Model, Diffusion of Innovation Theory, and community-based participatory research approach to create a community-centered dissemination framework. Consumer research, through focus groups and community-wide surveys, was centered on the adopters to ensure a multilevel intervention was well designed and effective. RESULTS: Collaboration took place between an academic institution and eight community-based organizations. These groups worked together to conduct thorough consumer research. A sample of 72 Asian Americans participated in 8 focus groups, and differences were noted across ethnic groups. Furthermore, 464 community members participated in an Individual Client Survey. Most participants agreed that early detection of cancer was important (434/464, 93.5%), cancer could happen to anyone (403/464, 86.9%), CRC could be prevented (344/464, 74.1%), and everyone should screen for CRC (389/464, 83.8%). However, 35.8% (166/464) of participants also felt that people were better off not knowing it they had cancer, and 45.5% (211/464) would screen only when they had symptoms. Most participants indicated that they would screen upon their doctor's recommendation, but half reported that they only saw a doctor when they were sick. Data collection currently is underway for a multilevel intervention (community health advisor and social marketing campaign) and will conclude March 2016. We expect that analysis and results will be available by June 2016. CONCLUSIONS: This study outlines a complementary role for researchers and community organizations in disseminating EBP, and incorporates social interactions and influences to move individuals from simple awareness to decisions towards positive action.
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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 | 0.058 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".