Leadership and Job Satisfaction in the Healthcare Sector: An Exploratory Study in Lebanon
Bibliographic record
Abstract
More and more, healthcare institutions work to ameliorate the relation supervisor/supervised. In hospitals, transformational leadership proved to influence employee’s motivation and satisfaction (Spinelli, 2006, p.20) thus the hospital’s services. To our knowledge, there is no study conducted on the administrative employees in the healthcare sector in Lebanon that constitute our sample. There is only one study conducted on nurses by El-Jardali et al., (2008) in 69 hospitals in this country. Given this situation, we how can describe the relationship between transformational leadership and employee’s job satisfaction in hospitals? Data processing of a questionnaire administered to 455 employees of 28 over 125 hospitals in Lebanon shows that there is no significant relationship between the employee’s job satisfaction and these two transformational leadership components: leader’s idealized influence and intellectual stimulation. While we found a correlation between employee’s job satisfaction and two other components: inspirational motivation (Training; projects monitoring) and individualized consideration (Active listening to employee’s work issues).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".