A time-lagged analysis of the effect of authentic leadership on workplace bullying, burnout, and occupational turnover intentions
Bibliographic record
Abstract
Destructive interpersonal experiences at work result in negative feelings among employees and negative work outcomes. Understanding the mechanisms through which bullying can lead to burnout and subsequent turnover is important for preventing and managing this problem. Leaders play a key role in shaping positive work environments by discouraging negative interpersonal experiences and behaviours. The aim of this study is twofold. Specifically we aim to examine the relationship between authentic leadership and new graduate nurses experiences of workplace bullying and burnout over a 1-year timeframe in Canadian healthcare settings. Furthermore we aim to examine the process from workplace bullying to subsequent burnout dimensions, and to job and career turnover intentions. Results of structural equation models on new graduate nurses working in acute care settings in Ontario (N = 205) provide support for the hypothesized model linking supervisor's authentic leadership, subsequent work-related bullying, and burnout, and these in turn to job and career turnover intentions. Thus, the more leaders were perceived to be authentic the less likely nurses’ were to experience subsequent work-related bullying and burnout and to want to leave their job and profession. The results highlight the important role of leadership in preventing negative employee and organizational 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".