Dysfunctional Coping Mediates the Relationship between Stress and Mental Health in Health-Care Staff Working amid the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: This cross-sectional study aimed to assess the stress outcomes in health-care staff working during the COVID-19 pandemic and to explore the role of coping in the relationship between stress outcomes and mental health dimensions with Preacher & Hayes's mediation analysis. SUBJECTS AND METHODS: One hundred seventy participants including physicians (n = 41; 24.1%), nurses (n = 114, 67.1%), and paramedics (n = 15, 8.8%) with a mean age of 37.69 ± 12.23 years and an average seniority of 14.40 ± 12.32 years were administered the Toronto Alexithymia Scale-20, Cohen's Perceived Stress Scale (PSS-10), the Emotional Processing Scale, and Positive and Negative Affect Schedule. The data were analyzed by estimation of simple correlation coefficients and a Preacher and Hayes's mediation procedure. RESULTS: Participants reported elevated levels of stress (7-8 sten on the sten scale developed for the PSS-10 questionnaire). Statistically significant differences in the stress levels between nurses, paramedics, and physicians could not be determined. In contrast, significant association between mental health outcomes and the occupational category could not be found. CONCLUSION: Our observations support the assumption about a controlling role of coping in the relationship between work-related stress, alexithymia, emotional processing loneliness and positive/negative affect in medical staff working amid pandemic.
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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".