Authentic leadership of preceptors: predictor of new graduate nurses' work engagement and job satisfaction
Bibliographic record
Abstract
AIM: To examine the relationships between new graduate nurses' perceptions of preceptor authentic leadership, work engagement and job satisfaction. BACKGROUND: During a time when the retention of new graduate nurses is of the upmost importance, the reliance on preceptors to facilitate the transition of new graduate nurses is paramount. METHODS: A predictive non-experimental survey design was used to examine the relationships between study variables. The final sample consisted of 170 randomly selected Registered Nurses (RNs) with <3 years experience and who worked in an acute care setting. RESULTS: Hierarchical multiple regression demonstrated that 20% of the variance in job satisfaction was explained by authentic leadership and work engagement. Furthermore, work engagement was found to partially mediate the relationship between authentic leadership of preceptors and engagement of new graduate nurses. CONCLUSIONS: New graduate nurses paired with preceptors who demonstrate high levels of authentic leadership feel more engaged and are more satisfied. Engagement is an important mechanism by which authentic leadership affects job satisfaction. IMPLICATIONS FOR NURSING MANAGEMENT: Managers must be aware of the role preceptors' authentic leadership plays in promoting work engagement and job satisfaction of new nurses.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".