261 Driving community health and mental health programming through collaborative, ongoing community health needs assessments
Bibliographic record
Abstract
Objectives/Goals: The Research Education and Community Health (REACH) coalition proposes to develop the infrastructure for continuous and comprehensive collection of community health data to drive programs, education, and funding priorities across municipal agencies, institutions, and nonprofit organizations in Galveston County. Methods/Study Population: The workgroup through REACH will organize and adopt a comprehensive community health needs assessment that 1) accumulates existing, readily available data for shared use (e.g., Center for Health Care Data at the UT School of Public Health, the Texas Department of State Health Services Center for Health Statistics, and Epic Cosmos, a data aggregation tool, used by UTMB and other health systems to improve patient care); 2) utilizes data collected throughout the community (i.e. non-profits, municipal agencies, and law enforcement); and 3) applies qualitative data from focus groups and/or key informant interviews, so we can hear directly from community members about what their needs are. By doing so, we hope all can benefit from having access to current and relevant data to drive our programs, education, and funding. Results/Anticipated Results: This Community Health Needs Assessment is being coordinated by a diverse workgroup including community organizations, researchers, and policy makers who will benefit from access to current and relevant data. The Galveston Youth Risk Student Survey, completed every three years and most recently in 2024, revealed lingering health and mental health effects of the COVID-19 pandemic on County youth. This highlighted the need for community access to current, accurate, and ongoing data to drive programming, interventions, and education. The REACH Coalition, made up of 23 UTMB Centers and Institutes and 39 community organizations, is spearheading this effort as a part of its mission to facilitate collaborative research, service, and educational efforts. Discussion/Significance of Impact: Collected data will be used to establish and support ongoing, coordinated interventions in response to identified needs. Shared ownership of data and project implementation optimizes resources and reduces gaps and/or redundancy in community programming.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".