Who Writes the Biggest Check for Charitable Care: A Comparison of For-Profit, Not-for-Profit, and Government Hospitals
Bibliographic record
Abstract
The current environment in the United States surrounding health care issues such as spending, costs, access, and affordability points toward a societal obligation to help provide for those who cannot pay the costs of their own care. Hospitals are often one of the largest employers in communities, and like many other organizations, view providing charitable care as an aspect of their corporate social responsibility (CSR). This study compares the recent levels of charitable care of for-profit, not-for-profit, and government hospitals. The authors attempt to determine which type of hospital is the most charitable, what the relationship between CSR and profitability may be, and the differences in the relationship between CSR and profitability for various hospital types. Data from a sample of 167 short-term, general hospitals were examined and results indicated that there were significant differences in CSR for government, not-for-profit and for-profit. Higher levels of CSR did not affect firm profitability, although significant interactions were found between control and CSR for varying levels of profitability.
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".