Hospital Partnerships in Population Health Initiatives
Bibliographic record
Abstract
Hospitals are expected to fulfill a role in the communities they serve by improving the health of the population in the community as mandated in the Affordable Care Act. One way hospitals achieve this is to create partnerships with diverse organizations, such as local public health departments, state/federal agencies, and other health care organizations. The aim of this study is to examine characteristics of hospitals that developed partnerships based on improving population health. This study utilized the 2015 Population Health Survey, American Hospital Association Database, and Dartmouth Atlas of Health Care. Hospital characteristics included size, ownership status, part of a system, teaching status location, Medicare percentage, Medicaid percentage, average stay length, and inpatient days per 1000 persons. Level of partnership was measured by the hospital's current working relationship with other hospitals/health care systems or local/state/other agencies. Univariate, bivariate, and multivariate regression analyses were used to analyze the relationship between hospital partnerships and organizational characteristics. Hospitals with strong relationships tend to be larger and not-for-profit hospitals, hospitals with system members and located in urban areas, and teaching-affiliated hospitals. This study also found hospital characteristics were related to hospitals' partnerships. Hospitals within health care systems and with high inpatient volume were more likely to report relationships that were stronger. This study provides a systematic and updated look at hospitals' partnership when looking at commitment to population health improvement and contributes to the literature by informing about the greater need to support rural and smaller hospitals with population health outreach activities.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".