Does CEO compensation impact patient satisfaction?
Bibliographic record
Abstract
PURPOSE: This study examines the relationship between CEO compensation and patient satisfaction in Ontario, Canada. The purpose of this paper is to determine what impact hospital CEO compensation has on hospital patient satisfaction. DESIGN/METHODOLOGY/APPROACH: The analyses in this study were based on data of 261 CEO-hospital-year observations in a sample of 103 nonprofit hospitals. A number of linear regressions were conducted, with patient satisfaction as the dependent variable and CEO compensation as the independent variable of interest. Controlling variables included hospital size, type of hospital, and frequency of adverse clinical outcomes. FINDINGS: CEO compensation does not significantly influence hospital patient satisfaction. Both patient satisfaction and CEO compensation appear to be driven primarily by hospital size. Patient satisfaction decreases, while CEO compensation increases, with the number of acute care beds in a hospital. In addition, CEO compensation does not even appear to moderate the influence of hospital size on patient satisfaction. RESEARCH LIMITATIONS/IMPLICATIONS: There are several limitations to this study. First, observations of CEO-hospital-years in which annual nominal CEO compensation was below $100,000 were excluded, as they were not publicly available. Second, this research was limited to a three-year range. Third, this study related the compensation of individual CEOs to a measure of performance based on a multitude of patient satisfaction surveys. Finally, this research is restricted to not-for-profit hospitals in Ontario, Canada. PRACTICAL IMPLICATIONS: The findings seem to suggest that hospital directors seeking to improve patient satisfaction may find their efforts frustrated if they focus exclusively on the hospital CEO. The findings highlight the need for further research on how CEOs may, through leading and supporting those hospital clinicians and staff that interact more closely with patients, indirectly enhance patient satisfaction. ORIGINALITY/VALUE: To the best of the authors' knowledge, no research has examined the relationship between hospital CEO compensation and patient satisfaction. This research fills the gap and provides a basis for future research.
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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".