Effects of social support on professors’ work stress
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how various types of workplace social support from different support sources interact with occupational stressors to predict the psychological well‐being of university professors. Design/method/approach A total of 99 full‐time professors participated via an online or paper questionnaire. Findings Using moderated hierarchical multiple regressions, the results support the hypotheses that the effects of occupational stressors on professors’ psychological well‐being vary depending on the level of perceived workplace social support. However, although workplace social support buffered the effects of some occupational stressors (i.e. work overload), social support exacerbated the adverse effects of others (i.e. decision‐making ambiguity). Research limitations/implications The dichotomous effects of social support suggest that the impact of social support may be moderated by another variable, such as perceived control over the stressor at hand. The present findings echo calls for further refinements to models of social support to examine how individuals’ situational appraisals shape the variable interactive effects of stressors and social support on individuals’ health and well‐being. Originality/value This study provides new insight into academic work stress by systematically examining the effects of workplace social support on professors’ work stress experience. This study also extends our current understanding of the relationships among stressors, strains, and social support by providing empirical evidence that workplace social support is neither consistently beneficial nor a unidimensional construct.
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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 | 0.015 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".