Under Pressure: Employee Work Stress, Supervisory Mentoring Support, and Employee Career Success
Bibliographic record
Abstract
ABSTRACT Despite consistent findings that stressed employees benefit from social support, these employees do not always have access to such support. We propose and test a conceptual model suggesting employee work stress will negatively affect supervisory career and psychosocial mentoring support. Drawing from social exchange theory, we predict this will indirectly affect employee career success (lower career satisfaction and promotability ratings, fewer promotions), and that the relationship between employee work stress and lower supervisory mentoring support can be explained by lower levels of work engagement experienced by, and attributed to, stressed employees. We tested our model across three studies. In Study 1, we collected four waves of multisource field data (254 employees, 127 managers, and company records) at a large postal organization in the United Kingdom (UK). Employee work stress was negatively related to supervisor career and psychosocial mentoring support, and indirectly affected career satisfaction and manager promotability ratings of employees via supervisor career mentoring support. Cross‐lagged panel analyses in a supplemental study additionally supported the proposed directionality of relationships. Study 2 included data across three waves from employees in Hong Kong (n = 137) and showed that employee work stress had indirect effects on supervisor career and psychosocial mentoring via lower employee engagement. In Study 3, using data from supervisors in the UK (n = 240) we showed that supervisor perceived employee stress had indirect effects on their provision of supervisor career and psychosocial mentoring support via lower perceived employee engagement.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".