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Does CEO Compensation Impact Hospital Patient Satisfaction?

2013· article· en· W2035274024 on OpenAlexaffabout
Kunle Akingbola, Herman A. van den Berg

Bibliographic record

VenueAcademy of Management Proceedings · 2013
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsLakehead University
Fundersnot available
KeywordsCompensation (psychology)Patient satisfactionNursingPsychologyJob satisfactionPatient careMedicineBusinessFamily medicineSocial psychology

Abstract

fetched live from OpenAlex

Patient satisfaction is central to the mission of hospitals lacking a profit motive. One would therefore expect patient satisfaction to be a priority of nonprofit hospital CEOs and a strong relationship between CEOs compensation and hospital patient satisfaction. Based on data of 261 CEO-hospital-year observations in a sample of 103 nonprofit hospitals in Ontario, Canada, this study examines the relationship between CEO compensation and patient satisfaction. The objective of this study was to determine what impact hospital CEO compensation has on hospital patient satisfaction. The findings suggest that 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 compensations 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. The findings seem to suggest that hospital directors seeking to improve patient satisfaction may be find their efforts frustrated if they focus them exclusively on the hospital CEO. The findings argue for further research examining how CEOs may, through leading and supporting those hospital clinicians and staff that interact more closely with patients, indirectly enhance patient satisfaction.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.031
GPT teacher head0.375
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2013
Admission routes2
Has abstractyes

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