Employee job satisfaction at Florida for-profit and not-for-profit hospitals
Bibliographic record
Abstract
High quality health care requires competent, motivated, and satisfied health care employees. This research examines whether employee job satisfaction differs at for-profit (FP) and not-for-profit (NFP) hospitals and how other organizational characteristics mediate this relationship. In this cross-sectional study, Press Ganey Employee Partnership Survey data from 35 Florida hospitals were used to understand the relationship between hospital ownership (primary independent variable) and employee job satisfaction (outcome). A flexible structural equation model was used to examine the relationship. The sample included 32,892 valid responses (approximately 23% from FP hospitals). Employees in FP hospitals were found to less satisfied with their jobs than their NFP counterparts. This trend was strongly associated with an inverse relationship between job satisfaction and assessment of immediate supervisors. The resulting job satisfaction model had an R2 of 0.524, indicating good fit. Further analyses revealed a positive association between perceived staffing levels and supervisor satisfaction, suggesting that the relative leanness of FP institutions might explain the observed difference in supervisor satisfaction. Employee job satisfaction is a complex multifaceted construct. Four main organizational factors affect employee job satisfaction: the organization’s ownership type (FP or NFP), employee relationships with supervisors, work schedule, and length of employment. Leaders need to provide front line supervisors with adequate resources and support. Training immediate supervisors how to approach and be supportive of their workers provides an immediate solution toward increasing employee job 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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".