Perceptions of preceptors' authentic leadership and final year nursing students' self-efficacy, job satisfaction, and job performance
Bibliographic record
Abstract
BACKGROUND: Preceptors are integral to the transition process of student nurses to licensed nurses. Preceptors are leaders who could utilize authentic leadership to help foster self-awareness and positive relationships and build capacity with student nurses. PURPOSE: Investigating the relationship between perceived preceptor authentic leadership and final year nursing students' self-efficacy, job satisfaction and performance. METHODS: This correlational study used data collected from 94 pre-licensure final semester baccalaureate and licensed practical nursing students from three different schools about preceptors' authentic leadership, self-efficacy, job satisfaction and performance. Mediated multiple regression analysis was used to examine the association between perceived preceptors' authentic leadership, self-efficacy, job satisfaction and performance. RESULTS: Preceptors were perceived to demonstrate authentic leadership (M = 3.21, SD = 0.68). Students' self-efficacy increased post preceptorship (t(93) = 3.96, p < .001), and authentic leadership was associated with self-efficacy (r = 0.46, p < .001) and job satisfaction (r = 0.49, p < .001). Self-efficacy mediated the relationship between job performance and authentic leadership. CONCLUSIONS: Authentic leadership has positive implications for preceptorship and nursing students' self-efficacy, job satisfaction and performance, which could enhance nurse retention.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".