“We Make the Path by Walking It”: Building an Academic Community Partnership With Boston Chinatown
Bibliographic record
Abstract
BACKGROUND: The potential for academic community partnerships are challenged in places where there is a history of conflict and mistrust. Addressing Disparities in Asian Populations through Translational Research (ADAPT) represents an academic community partnership between researchers and clinicians from Tufts Medical Center and Tufts University and community partners from Boston Chinatown. Based in principles of community-based participatory research and partnership research, this partnership is seeking to build a trusting relationship between Tufts and Boston Chinatown. OBJECTIVES: This case study aims to provides a narrative story of the development and formation of ADAPT as well as discuss challenges to its future viability. METHODS: Using case study research tools, this study draws upon a variety of data sources including interviews, program evaluation data and documents. RESULTS: Several contextual factors laid the foundation for ADAPT. Weaving these factors together helped to create synergy and led to ADAPT's formation. In its first year, ADAPT has conducted formative research, piloted an educational program for community partners and held stakeholder forums to build a broad base of support. CONCLUSIONS: ADAPT recognizes that long term sustainability requires bringing multiple stakeholders to the table even before a funding opportunity is released and attempting to build a diversified funding base.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".