Toward a Better Understanding of Emotional Exhaustion Among First‐Line Nurse Managers: The Role of Psychological Stress, Presenteeism, and Leader Loyalty
Bibliographic record
Abstract
Aims: Based on the Conservation of Resources and Leader-Member Exchange theories, this study investigates the relationship between psychological stress and emotional exhaustion among first-line nurse managers (FLNMs), while also examining the mediating role of presenteeism (i.e., working while ill) and the moderating effect of leader loyalty on this relationship. Method: A survey-based study was carried out on 161 French FLNMs. A mediated moderation model was tested through bootstrap regression analyses using PROCESS Macro. Results: The analyses yielded three primary findings. First, psychological stress among FLNMs is positively related to their level of emotional exhaustion. Second, presenteeism is found to be an explanatory mechanism of the psychological stress-emotional exhaustion relationship. Third, leader loyalty was found to moderate the indirect link between psychological stress (i.e., through presenteeism) and emotional exhaustion. Conclusion: The results provide a better understanding of the relationship between psychological stress and emotional exhaustion in the case of French FLNMs. They also clarify the role of other mechanisms, such as presenteeism and leader loyalty, that may be involved. Practical Implications: Considering the pivotal role of FLNMs within hospitals, findings from this study raise awareness of the detrimental consequences of psychological stress and presenteeism on FLNMs' health, while also showing the constructive aspects of the relationships between FLNMs and their superiors.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".