Psychological hardiness, social support, and emotional labor among nurses in Iran during the COVID-19 pandemic: A cross-sectional survey study
Bibliographic record
Abstract
Our study of nurses in Tehran was conducted in June of 2020, when the lockdown from the pandemic had been implemented. Nurses had been faced with how to effectively manage their own emotion responses during patientcare. Our study aims to evaluate how psychological and social resources were jointly related to the use of emotional labor through surface acting and deep acting among nurses at public hospitals. The study design was a single-wave, cross-sectional self-report questionnaire survey containing validated measures where the nurses reported on their work experiences during the pandemic. The participants came from five out of 50 public hospitals within Tehran . Of the 250 nurses chosen by using multi-stage randomly sampling, 224 were retained after listwise deletion of missing data and outliers. Through a survey questionnaire, participants responded to scale measures of psychological hardiness, social support, and emotional labor to investigate the joint impact of hardiness and social support on emotional labor. Their responses provided information on the (1) validity and reliability of all variables, and (2) the hypothesized structural relations, using SPSS -AMOS 22 software. Challenge and control were related to social support; coworker sympathy and supervisory support were related to surface acting; coworker sympathy was related to deep acting. Under strong support, high hardiness was most negatively related to surface acting and positively related to deep acting. Through coworker and supervisory support, hardiness became an effective means for nurses to regulate their own emotions during interactions to enhance patientcare.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".