The essential impact of stress appraisals on work engagement
Bibliographic record
Abstract
This paper explains the contradictory findings on the relationship between stress and work engagement by including appraisals as a driving mechanism through which job stressors influence engagement. In doing so, it explores whether stressors categorised as either challenging or hindering can be appraised simultaneously as both. Second, it investigates whether stress mindset explains not only how stressors are appraised, but also how appraisals influence engagement. Over five workdays, 487 Canadian and American full-time employees indicated their stress mindset and appraised numerous challenging and hindering stressors, after which they self-reported their engagement at work. Results showed that employees rarely appraised stress as uniquely challenging or hindering. Moreover, when employees harbored positive views about stress, stressors overall were evaluated as less hindering and hindrance stressors were particularly more challenging. Stress mindset appears to be critical in modulating the genesis of stress appraisals. In turn, appraisals explained the stressor-engagement relationship, with challenge and hindrance stressors boosting and hampering engagement, respectively. Finally, positive stress mindset buffered the negative effect of hindrance appraisals on engagement. Our findings clarify misconceptions about how workplace stressors impact engagement and offer novel evidence that stress mindset is a key factor in 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".