The double-edged sword of manager caring behavior: Implications for employee wellbeing.
Bibliographic record
Abstract
While managers play a critical role in supporting employee wellbeing, prior research suggests that doing so can take a toll on managers themselves. However, we know little about the potential implications of this for employees. Drawing from the leadership-wellbeing literature and social psychological theories of guilt, we propose that manager caring behavior is associated with both positive (vitality) and negative (guilt) employee wellbeing. We find support for these relationships in Study 1 (N = 264) with a time-separated survey. In Study 2, we replicate these findings, and in addition, we examine a negative perceptual response to manager care: employee-rated manager role overload. Drawing on perceptual salience research, we propose that the negative relationship between manager care and employee-rated manager role overload is exacerbated in a team environment where employees fail to care for each other (i.e., a weak caring climate). Study 2 (N = 360) largely supports our hypotheses with multilevel, time-separated field data. The findings suggest that managers should not be expected to "go it alone" to support employee wellbeing because doing so may relate negatively to employee outcomes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.003 | 0.027 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".