Work-Related Stress, Psychological Well-Being, and Work Engagement
Bibliographic record
Abstract
The purpose of this chapter is to try to understand how stress, psychological well-being, and work engagement affect the quality of working life at public universities in the Brazilian state of Minas Gerais and in Quebec. As such, we describe the effects on and relationships between work-related stress, workload, psychological well-being, work engagement and the QoWL of university professors, trying to identify the main differences in perception regarding these variables in both groups. The sample comprised 274 professors from Minas Gerais and 252 from Quebec. Data were collected using an online questionnaire sent through the Survey Monkey to six universities in the first semester of 2013. The data were analyzed using at SPSS version 21. The reliability analysis showed that the tested variables are consistent with Morin’s model (2008), allowing us to assess QoWL at universities. There were significant differences in the average perceptions of Minas Gerais and Quebec professors for work-related stress, physical and mental workload and number of daily hours worked. In order to further explore these results, we used a linear regression analysis that showed that physical workload, psychological well-being and gender determine the work-related stress scores.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".