214 Enhancing community-engaged research through the adaptation and integration of the Chicago Citizen Scientist Program
Bibliographic record
Abstract
Objectives/Goals: Citizen Science (CS) recognizes the vital role that community members play in research, centering their unique lived experiences and perspectives across the research cycle. We aim to enhance community-engaged research (CEnR) by adapting a CS Program at the University of Illinois Chicago (UIC) Center for Clinical and Translational Science (CCTS). Methods/Study Population: The CS Program, launched in response to COVID-19, was designed/piloted for Chicago community members interested in research careers, developing evidence-based practice skills, and/or partnering with academic, community, and/or public health organizations. To inform program adaptation, we are conducting a landscape assessment, including 1) inventory/annotation of existing curricular materials, 2) review of peer-reviewed literature, 3) website extraction of existing CS programs’ key components, and 4) interviewing key informants. An Advisory Board of prior CS instructors/alumni will guide curriculum adaptation, coordination, and fidelity. We will also identify strategic internal/external UIC organizational partnerships to collaborate on establishing, developing, and conducting the program. Results/Anticipated Results: Literature describes common CS program typology as a continuum, from research done “with the people” to research conducted “by the people” (King et al, 2016). Our program will equip CS to engage across these conceptual continuums. We plan to launch the UIC CCTS CS Program by Fall 2025 and have 10 online modules with a disability justice lens. Topics will range from Critical Thinking and the Research Process to Structural Violence and Evaluation Frameworks. Grounded in liberatory pedagogy, sessions will be taught by UIC faculty, staff, and community partners, each containing a lecture, interactive activities, and assessments. Participants will earn a certificate applicable to related jobs (e.g., academic/community research), supplement community health worker training, precursor to health degrees, and more. Discussion/Significance of Impact: Through the CS Program, we aim to center community expertise and lived experience within research, foster bi-directional collaborations and relationships, and build community capacity. We are evaluating this project adaptation and implementation to create a blueprint for institutions to enhance their community-engaged research.
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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 | 0.035 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".