Mindfulness During the COVID-19 Pandemic Lockdowns: Intolerance Uncertainty and Psychological Well-Being Among Employees
Bibliographic record
Abstract
The COVID-19 pandemic has increased uncertainty worldwide, which has various negative impacts on psychological well-being. In times like these, it is important to explore how individual resources such as trait mindfulness would help people deal with uncertainty. The aim of the current study was to examine the role of intolerance to uncertainty (IU) as a mediator between trait mindfulness and psychological well-being, including stress, anxiety, depression, and emotional burnout, among employees. Two hundred ninety-three employees completed an online self-report questionnaire during the first COVID-19 pandemic lockdowns in Turkey. The nonparametric bootstrap procedure in AMOS 26.0 was used to test the proposed model. The findings indicated full mediation between trait mindfulness and psychological well-being measures among employees. In other words, employees who reported higher levels of mindfulness perceived their current situation as less threatening, and they were able to tolerate uncertainty, which decreased participants’ stress, anxiety, depression, and emotional burnout. The findings are important for understanding the impact of mindfulness on the psychological well-being of people and the role of intolerance uncertainty in this relationship. The results will be useful for the development of new interventions to promote resources that will increase individual awareness and control during difficult circumstances.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 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 teacher head, 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".