The Impact of Rural Hospital Closures and Mergers on Health System Ecologies: A Scoping Review
Bibliographic record
Abstract
Abstract Despite playing a pivotal role in rural community health services delivery and in local economies, rural hospitals in the United States have closed or merged with larger health networks at alarming rates. This scoping review examines what is known about the impacts of rural hospital closures and mergers, synthesizing the literature across 2010-2024. Most of the identified studies examined closures, primarily reporting on community impacts. Using this literature, we inductively derived a new Health System Ecologies Impact Matrix research tool to assess knowledge related to health system changes. Knowledge gaps remain related to financial, workforce, and utilization-related outcomes, and little is known about closure impacts on neighboring hospitals and communities. Few studies report effects of rural hospital mergers, with those analyses primarily focusing on financial and utilization outcomes for the merged hospital. No studies examined the impacts of rural hospital mergers on patients or individuals and their social environments. Protocol The registered scoping review protocol may be accessed on OSF: https://doi.org/10.17605/OSF.IO/GEKJC
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".