Assessing Priorities in a Statewide Cardiovascular and Diabetes Health Collaborative Based on the Results of a Needs Assessment: Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: The Ohio Cardiovascular and Diabetes Health Collaborative (Cardi-OH) unites general and subspecialty medical staff at the 7 medical schools in Ohio with community and public health partnerships to improve cardiovascular and diabetes health outcomes and eliminate disparities in Ohio's Medicaid population. Although statewide collaboratives exist to address health improvements, few deploy needs assessments to inform their work. OBJECTIVE: Cardi-OH conducts an annual needs assessment to identify high-priority clinical topics, screening practices, policy changes for home monitoring devices and referrals, and preferences for the dissemination and implementation of evidence-based best practices. The results of the statewide needs assessment could also be used by others interested in disseminating best practices to primary care teams. METHODS: A cross-sectional survey was distributed electronically via REDCap (Research Electronic Data Capture; Vanderbilt University) to both Cardi-OH grant-funded and non-grant-funded members (ie, people who have engaged with Cardi-OH but are not funded by the grant). RESULTS: In total, 88% (103/117) of Cardi-OH grant-funded members and 8.14% (98/1204) of non-grant-funded members completed the needs assessment survey. Of these, 51.5% (53/103) of Cardi-OH grant-funded members and 47% (46/98) of non-grant-funded members provided direct clinical care. The top cardiovascular medicine and diabetes clinical topics for Cardi-OH grant-funded members (clinical and nonclinical) were lifestyle prescriptions (50/103, 48.5%), atypical diabetes (38/103, 36.9%), COVID-19 and cardiovascular disease (CVD; 38/103, 36.9%), and mental health and CVD (38/103, 36.9%). For non-grant-funded members, the top topics were lifestyle prescriptions (53/98, 54%), mental health and CVD (39/98, 40%), alcohol and CVD (27/98, 28%), and cardiovascular complications (27/98, 28%). Regarding social determinants of health, Cardi-OH grant-funded members prioritized 3 topics: weight bias and stigma (44/103, 42.7%), family-focused interventions (40/103, 38.8%), and adverse childhood events (37/103, 35.9%). Non-grant-funded members' choices were family-focused interventions (51/98, 52%), implicit bias (43/98, 44%), and adverse childhood events (39/98, 40%). Assessment of other risk factors for CVD and diabetes across grant- and non-grant-funded members revealed screening for social determinants of health in approximately 50% of patients in each practice, whereas some frequency of depression and substance abuse screening occurred in 80% to 90% of the patients. Access to best practice home monitoring devices was challenging, with 30% (16/53) and 41% (19/46) of clinical grant-funded and non-grant-funded members reporting challenges in obtaining home blood pressure monitoring devices and 68% (36/53) and 43% (20/46) reporting challenges with continuous glucose monitors. CONCLUSIONS: Cardi-OH grant- and non-grant-funded members shared the following high-priority topics: lifestyle prescriptions, CVD and mental health, family-focused interventions, alcohol and CVD, and adverse childhood experiences. Identifying high-priority educational topics and preferred delivery modalities for evidence-based materials is essential for ensuring that the dissemination of resources is practical and useful for providers.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".