Authentic leadership and behavioral integrity as drivers of staff nurses' commitment and work engagement
Bibliographic record
Abstract
Background and aim: Authentic leadership is relatively a new paradigm that emphasizes leaders' credibility. It can serve the healthcare organization to build an organizational culture that reinforces the healthcare worker in being committed and engaged in work. This study aimed to examine the effect of authentic leadership on staff nurses' affective organizational commitment and work engagement through examining the mediating role of behavioral integrity.Methods: Study design: A descriptive correlational design was used in conducting this study. Setting: The study was conducted at two different hospitals, namely Menoufia University Hospital, and National Liver Institution, Egypt. Sampling: A Convenience sampling of two groups were selected; 64 front-line nurse managers and 320 staff nurses. Tools: Four instruments were used for collecting data; authentic leadership questionnaire, behavioral integrity scale, affective organizational commitment questionnaire, and Utrecht work engagement scale. Spearman correlation (r) and binary logistic regression analysis were used to achieve the study aim.Results: Total behavioral integrity (BI) score was significantly correlated with total authentic leadership (AL) and two of its subscales (internalized moral perspective and balanced processing). Moreover, a significant positive correlation was revealed between the enactment of espoused values at one hand and total authentic leadership and its subscales except for self-awareness. Correlation analysis between BI and organizational commitment revealed a significant positive correlation between the two variables. A significant positive correlation was also found between the BI total and its subscales with work engagement total and its subscales.Conclusions: This study supported the proposition that the relationship between authentic leadership and positive nurses’ outcomes including affective commitment and work engagement is mediated by behavioral integrity.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".