Visualizing Reach of Racial and Ethnic Approaches to Community Health for Asian Americans: the REACH FAR Project in New York and New Jersey
Bibliographic record
Abstract
The REACH FAR (Racial and Ethnic Approaches to Community Health for Asian Americans) project in New York and New Jersey is guided by a multisector coalition made up of the New York University Center for the Study of Asian American Health, the New York City Department of Health and Mental Hygiene, 4 communitybased organizations: UNITED SIKHS, serving the Asian Indian population; Diabetes Research, Education, and Action for Minorities, serving the Bangladeshi community; Kalusugan Coalition, Inc., serving the Filipino community; and Korean Community Services of Metropolitan New York, Inc., serving the Korean community; and other organizations and groups serving these communities.The map shows locations in 2016 of faith-based organizations that served as implementation sites for Keep on Track, an intervention consisting of nutrition and hypertension management/cardiovascular disease prevention strategies for Asian American communities.Implementation sites are located in Asian American population clusters.Lines on the map, representing the number of participants in the Keep on Track program, provide a visual representation of service delivery and reach of the REACH FAR project.The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions.www.cdc.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".