Unraveling the effects of occupational identity verification, self-esteem and identity salience on managers’ mental health: examining psychological distress and depression in the workplace
Bibliographic record
Abstract
Purpose Managers play a crucial role in organizations. They make decisions that directly influence organizational success and significantly impact employees’ mental health, development and performance. They are responsible for ensuring the financial well-being and long-term sustainability of organizations. However, their mental health is often overlooked, which can negatively affect employees and organizations. This study aims to address managers’ mental health at work, by examining specifically the direct and indirect effects of identity verification on their psychological distress and depression through self-esteem at work. The study also aims to examine the moderating as well as moderated mediation effects of identity salience. Design/methodology/approach A sample of 314 Canadian managers working in 56 different companies was studied, using multilevel analyses. Findings The findings showed that the verification of managers’ identity vis-à-vis recognition is positively associated with psychological distress and depression. Self-esteem completely mediates the association between low identity verification vis-à-vis work control and psychological distress, and also the association between low identity verification vis-à-vis work control and superior support and depression, while it partially mediates the association between low identity verification vis-à-vis recognition and depression. Practical implications This study can also help both managers and human resource management practitioners in understanding the role of workplaces in the identity verification process and developing relevant interventions to prevent mental health issues among managers at work. Originality/value This study proposed a relatively unexplored approach to the study of managers’ mental health at work. Its integration of identity theory contributes to expanding research on management and workplace mental health issues.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".