Leadership Competencies, Workplace Civility Climate, and Mental Well-being in El- Azazi Hospital for Mental Health, Egypt
Bibliographic record
Abstract
Background: In light of the coronavirus pandemic, a team leader’s ability to attain and maintain healthy workplaces is crucial. Nurse leaders should aim to mitigate workplace anxieties by promoting team cohesiveness, mutual support, and the wellbeing of members. Aim: This study was conducted at the beginning of COVID-19 pandemic to assess the effect of leadership competencies on workplace civility climate and mental well-being at an Egyptian hospital for mental health. Design: Descriptive correlation design was used. Tools: Measures used were leadership competencies, workplace civility climate, and the Warwick- Edinburgh mental wellbeing scale. Results: more than half of the sample were satisfied with leaders’ competencies, three quarter of them rated the workplace environment as respectful, and more than three quarter of them reported moderate or good mental wellbeing. Statistically significant correlations were found between leadership competencies and both workplace civility climate and mental well-being. Conclusion: leaders at El Azazi Hospital were assessed as proficient and providing a positive civility climate, but were not sensitive to the mental wellbeing of staff. Recommendation: Future research to investigate what specific factors affect mental well-being among psychiatric nurses rather than leadership < /div> competencies is recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".