The Essential Impact of Stress Appraisals on Job Performance and Work Engagement
Bibliographic record
Abstract
This paper investigates the effects of stressors on in-role performance and work engagement. First, it explores whether stressors traditionally categorized as either challenging or hindering can be appraised simultaneously as both. Second, it explains the contradictory findings surrounding the relationships between stress and performance and engagement by including stress appraisals as a mechanism driving these relationships. In doing so, it also looks at whether stress mindset could explain not only how stressors are appraised, but also how appraisals influence performance and engagement. Over five work days, 487 Canadian and American full-time employees were asked to indicate their stress mindset and appraise an array of challenging and hindering stressors, after which they evaluated their performance and engagement. Results showed that employees rarely appraised stress as uniquely challenging or hindering. In addition, when employees harbored positive views about stress, stressors overall were evaluated as less hindering and hindrance stressors in particular were more challenging. Stress mindset was thus shown to be critical in modulating the genesis of stress appraisals. In turn, appraisals were able to explain the stressors’ relationships to performance and engagement, with challenge and hindrance stressors boosting and hampering these outcomes, respectively. Finally, positive stress mindset buffered the negative influence of hindrance appraisals on engagement. Our findings clarify misconceptions about how workplace stressors are evaluated and offer novel evidence that stress mindset is a key variable in the study of stress at work.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".