Ohio Presents Opportunities For Understanding Hospital Alignment With Public Health Agencies On Community Health Assessments
Bibliographic record
Abstract
Multisector collaboration is critical for improving population health. Improving alignment between nonprofit hospitals and local health departments is one promising approach to achieving health improvement, and a number of states are exploring policies to facilitate such collaboration. Using public documents, we evaluated the alignment between Ohio nonprofit hospitals and local health departments in the community health needs they identify and those they prioritize. The top three needs identified by hospitals and health departments were mental health, substance use, and obesity. Alignment across organizations was high among the top needs, but it varied more among less commonly identified needs. Alignment related to social determinants of health was low, with health departments being more responsive to social determinants than hospitals. Given the different strengths and capacities of hospitals and health departments, this divergence may be in the best interests of the communities they serve. Community benefit policies should consider how to promote collaboration between hospitals and health departments while also encouraging organizations to use their own expertise to meet community needs.
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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 | 0.030 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".