Stress, Emotion Regulation, and Well-Being among Canadian Faculty Members in Research-Intensive Universities
Bibliographic record
Abstract
Existing research reveals the academic profession to be stressful and emotion-laden. Recent evidence further shows job-related stress and emotion regulation to impact faculty well-being and productivity. The present study recruited 414 Canadian faculty members from 13 English-speaking research-intensive universities. We examined the associations between perceived stressors, emotion regulation strategies, including reappraisal, suppression, adaptive upregulation of positive emotions, maladaptive downregulation of positive emotions, as well as adaptive and maladaptive downregulation of negative emotions, and well-being outcomes (emotional exhaustion, job satisfaction, quitting intentions, psychological maladjustment, and illness symptoms). Additionally, the study explored the moderating role of stress, gender, and years of experience in the link between emotion regulation and well-being as well as the interactions between adaptive and maladaptive emotion regulation strategies in predicting well-being. The results revealed that cognitive reappraisal was a health-beneficial strategy, whereas suppression and maladaptive strategies for downregulating positive and negative emotions were detrimental. Strategies previously defined as adaptive for downregulating negative emotions and upregulating positive emotions did not significantly predict well-being. In contrast, strategies for downregulating negative emotions previously defined as dysfunctional showed the strongest maladaptive associations with ill health. Practical implications and directions for future research are also discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".