Nuances of COVID-19 and Psychosocial Work Environment on Nurses’ Wellbeing: The Mediating Role of Stress and Eustress in Lieu to JD-R Theory
Bibliographic record
Abstract
BACKGROUND: The global spread of COVID-19 makes Pakistan as vulnerable as any other developing country and the risk posed by the weak health system increases the fears in people's minds. The government is strategically expanding the scope of community ownership and increasing understanding in the population through risk communication and engagement; still, the situation remains very austere and is even affecting the psychological health of caregivers. We, therefore, sought to determine the impact of psychosocial job demands and resources associated with the psychological health of nurses in a time lag duration of 3 months, i.e., since the start of the pandemic, from January to March 2020. We hypothesized the significant mediating roles of stress and eustress in a direct relationship with psychosocial work environment job demands, job resources, and nurses' wellbeing. METHODS: In this cross-sectional self-administrated study, we distributed the survey in two parts by using a time-lag strategy to collect data at the start of pandemic (Time 1) and then again 3 months later (Time 2). Data on 53 items was collected from 208 female nurses who had participated in both the time phases and met the eligibility protocols of the study (such as being certified female nurses who have a registration number (RN) through the Pakistan Nursing Council (PNC), having 4 years of a generic nursing degree, and 2 years of nursing experience). FINDINGS: 2. We found 29.0% variance, 0.05 and 0.03 effect size, and 0.153 predictive abilities on stress as explained by job demands, and 53.4% variance, 0.19 and 0.39 effect size, and 0.275 predictive abilities on eustress as explained by job resources. And finally, there was 71.2% variance, 0.00, 0.02, 0.02, 0.03, 0.42, and 0.07 effect sizes, and 0.545 predictive abilities on our third endogenous construct, wellbeing, which is explained by both the psychosocial job demands and job resource variables. From partial to full mediation, stress and eustress significantly impact the psychosocial work environment of nurses.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".