Authentic leadership and job satisfaction among long-term care nurses
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the effects of managers’ authentic leadership, person–job match in the six areas of worklife (AWLs) and emotional exhaustion on long-term care registered nurses’ job satisfaction. Design/methodology/approach A secondary analysis of baseline data from a national survey of 1,410 Canadian registered nurses from various work settings was used in this study, which yielded a subsample of 78 nurses working in direct care roles in long-term care settings. Hayes’ PROCESS macro for mediation analysis in SPSS was used to test the hypothesized model. Findings Findings showed that authentic leadership significantly predicted job satisfaction directly and indirectly through AWLs and emotional exhaustion. Practical implications Authentic leadership may provide guidance to long-term care managers about promoting nurses’ job satisfaction, which is essential to recruiting and retaining nurses to meet the care needs of an aging population. Originality/value As demand for care of the aged is increasing and creating challenges to ensuring a sufficient and sustainable nursing workforce, it is important to understand factors that promote long-term care nurses’ job satisfaction. Findings contribute to knowledge of long-term care nurses by suggesting that managers’ authentic leadership can positively affect nurses’ job satisfaction directly and indirectly through positive perceptions of AWLs and lower emotional exhaustion.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".