Hospitalists, two decades later: Which US hospitals utilize them?
Bibliographic record
Abstract
Hospitalists, or specialists of hospital medicine, have long been practicing in Canada and Europe. However, it was not until the mid-1990s, when hospitals in the U.S. started widespread adoption of hospitalists. Since then, the number of hospitalists has grown exponentially in the U.S. from a few hundred to over 50,000 in 2016. Prior studies on hospitalists have well documented benefits hospitals gain from adopting this innovative staffing strategy. However, there is a dearth of research documenting predictors of hospitals' adoption of hospitalists. To fill this gap, this longitudinal study (2003-2015) purposes to determine organizational and market characteristics of U.S. hospitals that utilize hospitalists. Our findings indicate that private not-for-profit, system affiliated, teaching, and urban hospitals, and those located in higher per capita income markets have a higher probability of utilizing hospitalists. Additionally, large or medium, profitable hospitals, and those that treat sicker patients have a higher probability of adoption. Finally, hospitals with a high proportion of Medicaid patients have a lower probability of utilizing hospitalists. Our results suggest that hospitals with greater slack resources and those located in munificent counties are more likely to use hospitalists, while their under-resourced counterparts may experience more barriers in adopting this innovative staffing strategy.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".