The influence of authentic leadership and empowerment on nurses’ relational social capital, mental health and job satisfaction over the first year of practice
Bibliographic record
Abstract
AIMS: To examine a theoretical model testing the effects of authentic leadership, structural empowerment and relational social capital on the mental health and job satisfaction of new graduate nurses over the first year of practice. BACKGROUND: Relational social capital is an important interpersonal organizational resource that may foster new graduate nurses' workplace well-being and promote retention. Evidence shows that authentic leadership and structural empowerment are key aspects of the work environment that support new graduate nurses; however, the mediating role of relational social capital has yet to be explored. DESIGN: A longitudinal survey design was used to test the hypothesized model. METHODS: One hundred ninety-one new graduate nurses in Ontario with <2 years of experience completed mail surveys in January-March 2010 and 1 year later in 2011. Path analysis using structural equation modelling was used to test the theoretical model. RESULTS: Participants were mostly female, working full time in medicine/surgery or critical care. All measures demonstrated acceptable reliability and validity. Path analysis results supported our hypothesized model; structural empowerment mediated the relationship between authentic leadership and nurses' relational social capital, which in turn had a negative effect on mental health symptoms and a positive effect on job satisfaction. All indirect paths in the model were significant. CONCLUSION: By creating structurally empowering work environments, authentic leaders foster relational social capital among new graduate nurses leading to positive health and retention outcomes.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".