Assessing multilevel barriers and facilitators to implementing strategies for cancer screening among Asian Americans in federally qualified health centers: a case study of a community–clinic partnership to improve care for safety-net patients
Bibliographic record
Abstract
BACKGROUND: Cancer screening disparities among medically underserved Asian Americans are well documented. Assessing determinants of success in implementing multilevel cancer screening strategies in safety-net settings is critical to improve screening and cancer equity. METHODS: Academic, clinic, and community partners established the Advancing Care Together (ACCT) formal network to implement multilevel strategies that promote cancer screening among low-income Chinese, Korean, and Vietnamese adults in Orange County, California. ACCT focused on breast, cervical, and colorectal cancer. From August 2018 to January 2021, meetings, surveys, and interviews were conducted with community and clinic partners before implementing evidence-based strategies (EBS) such as educational workshops and community navigation, aligned with cultural and linguistic factors, to increase cancer screening. We evaluated formative data, collected during meetings and interviews and via patient navigator intake forms, to identify barriers and facilitators to implementing EBS in Asian-serving community clinics. We assembled a code book, aligned with the exploration, preparation, implementation, and sustainment framework to guide data analysis of implementation determinants of cancer screening. RESULTS: During the implementation of cancer screening EBS, ACCT staff and community navigators identified barriers in the inner context (lack of language-concordant providers, staff turnover) and outer context (referral wait times, transportation, and cultural stigma). Academic and community partnerships can support multilevel EBS to increase cancer screening (bridging factors). Additional support for clinic and quality improvement staff may be needed to evaluate cancer screening outcomes, and routine training on evaluating electronic medical records is needed (innovation factors). CONCLUSION: Community-clinic-academic partnerships can increase cancer screening and awareness in Asian American communities, including addressing cultural screening barriers and identifying adaptation needs for educational materials. Additionally, longstanding clinic- and community-level barriers persist in federally qualified health centers serving underrepresented Asian American communities. These barriers in the cancer screening process include high turnover among clinic quality improvement teams and difficulty prioritizing cancer screening throughout the COVID-19 pandemic.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".