Perceived Organizational Support and Employee Well-Being During Crisis: A Study of the COVID-19 Pandemic
Bibliographic record
Abstract
POS has previously been found to be a protective factor against adverse wellbeing outcomes; however, questions remain concerning the mechanisms by which POS relates to employee well-being.To inform this gap, the current study examined different mechanisms by which POS might influence the well-being of employees occupying a range of job types during a crisis of an unprecedented nature, which resulted in stressors spanning work, family, and health domains, and impacted a range of personal-related well-being outcomes, including anxiety, depression, emotional exhaustion, loneliness, and sleep problems.The current study examined three mechanisms hypothesized in the literature: the direct effect of POS on strains, the role of POS as a buffer in the relationship between stressors and strains, and the indirect effect of POS on strains through the coping strategies in which employees engage.The moderating role of demographic factors was also investigated.Results indicate that all three mechanisms are present at the same time, though not to the same extent: the direct role of POS on strains and the indirect role of POS on strains through the coping strategies in which employees engaged were widely supported.In contrast, the buffering role of POS was supported in only a small number of stressor-strain relationships examined.Moreover, although gender and age were not found to moderate the relationship between POS and strains, job level was found to moderate the relationship between POS and anxiety, depression, emotional exhaustion, and sleep problems.These finding may inform the development of organizational interventions to mitigate the negative impact of an acute crisis on the wellbeing of employees.Implications for practice and research, and recommendations for future research are also discussed.guidance and encouragement at every stage of the dissertation process.Her insightful feedback, positive energy, and academic excellence were central to the successful completion of this dissertation.I am also thankful to Dr.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".