The influence of authentic leadership and supportive professional practice environments on new graduate nurses’ job satisfaction
Bibliographic record
Abstract
It is important to uncover new approaches to attracting and retaining newly qualified nurses in Canada to address the growing nursing workforce shortage. Authentic leadership theory proposes mechanisms that allow managers to create positive and supportive environments that facilitate new graduate nurses’ transition into practice and subsequently improve nurses’ and organizational outcomes. The purpose of this study was to test a theoretical model linking authentic leadership to new graduate nurses’ job satisfaction through its effect on supportive professional practice environments. A secondary analysis of data ( n = 93) from a larger study of new graduate nurses in their first two years of practice was conducted. Mediation multiple regression analysis was performed to determine the influence of authentic leadership and supportive professional practice environments on new graduate nurses’ job satisfaction. Measures of the Authentic Leadership Questionnaire (ALQ), the Revised Nursing Worklife Index (NWI-R) and the North Carolina Center for Nursing – Survey of Newly Licensed Nurses (NCCN-SNLN) were used. Supportive professional practice environment partially mediated the relationship between authentic leadership and new graduate nurses’ job satisfaction. The findings suggest that managers who demonstrate authentic leadership create supportive professional practice environments and are more likely to enhance new graduate nurses’ job satisfaction.
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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 | 0.009 |
| 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) | 0.001 | 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".