How do workplace stressors during COVID-19 affect health frontline employees in Iran: Investigating the role of employee resilience and constituent attachment
Bibliographic record
Abstract
Purpose The purpose of the present study is to extend the body of research on healthcare management by examining the effect of workplace stressors, including abusive supervision, customer incivility and the perceived threat of COVID-19 (PCT), on turnover intention. The study also contributes to healthcare management research by examining the mediating role of emotional exhaustion, the moderating role of employee resilience and constituent attachment. Design/methodology/approach The study developed and tested a model explaining the relationship between abusive supervision, customer incivility, PCT, emotional exhaustion, turnover intention, employee resilience and constituent attachment. Data were collected from a sample of 375 frontline employees who work in private hospitals in Mashhad, the second-most populous city in Iran. Findings The findings indicate that abusive supervision and customer incivility, directly and indirectly, affect turnover intention through emotional exhaustion. Furthermore, employee resilience was found to mitigate the relationship between stressors excluding the PCT and emotional exhaustion. Moreover, constituent attachment decreased the likelihood of turnover intention among employees who experienced abusive supervision. The findings suggest that controlling abusive supervision, customer incivility and PCT can lead to less emotionally exhausted employees with lower turnover intention. Furthermore, enhancing employee resilience and constituent attachment can decrease emotional exhaustion and turnover intention. Originality/value Despite the large body of research on the relationship between the variables mentioned above, few studies have presented a conceptual model based on the relationship between them. This article presents a conceptual model that has not been previously discussed in any other publication to examine the moderating effect of organizational and individual factors in the relationship between workplace stressors and their consequences, which have not been widely covered in existing literature. Drawing upon conservation of resources theory, job embeddedness theory and attachment theory, the present study aims to fill this gap in the literature.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".