Emotional labor and burnout among nurses in Iran: core self-evaluations as mediator and moderator
Bibliographic record
Abstract
BACKGROUND: This study investigated the mediating and moderating impact of core self-evaluations in the path from emotional labor to burnout. Our hypothesized associations are based on Hobfoll (Rev Gen Psychol 6:307-24, 2002) conservation of resources theory. METHOD: Three hundred nurses from four hospitals in Abadan, Iran, were invited to participate in our study. Of the 300, 255 completed all sections and questions in our survey for an 85% response rate. The posited direct and indirect effects were evaluated with structural equation modeling and the interaction effects were evaluated with hierarchical moderated regression and simple regression slope plots. RESULT: Deep acting has indirect effects on burnout through core self-evaluations. Though unrelated to surface acting, core self-evaluations moderate its impact: under low core self-evaluations, surface acting is strongly related to emotional exhaustion and inversely related to personal accomplishment, whereas, under high core self-evaluations, surface acting is unrelated to these burnout dimensions. CONCLUSION: Our findings reveal the dual functions of CSE as a psychological resource and buffer to offset the interpersonal demands of patient care. Limitations, directions for future research, and practical implications are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 | 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".