A Framework for Protecting and Promoting Employee Mental Health through Supervisor Supportive Behaviors
Bibliographic record
Abstract
The attention to workplace mental health is timely given extreme levels of burnout, anxiety, depression and trauma experienced by workers due to serious extraorganizational stressors - the COVID-19 pandemic, threats to climate change, and extreme social and political unrest. Workplace-based risk factors, such as high stress and low support, are contributing factors to poor mental health and suicidality (Choi, 2018; Milner et al., 2013, 2018), just as low levels of social connectedness and belonging are established risk factors for poor mental health (Joiner et al., 2009), suggesting that social support at work (e.g., from supervisors) may be a key approach to protecting and promoting employee mental health. Social connections provide numerous benefits for health outcomes and are as, or more, important to mortality as other well-known health behaviors such as smoking and alcohol consumption (Holt-Lundstad et al., 2015), and can serve as a resource or buffer against the deleterious effects of stress or strain on psychological health (Cohen & Wills, 1985). This manuscript provides an evidence-based framework for understanding how supervisor supportive behaviors can serve to protect employees against psychosocial workplace risk factors and promote social connection and belongingness protective factors related to employee mental health. We identify six theoretically-based Mental Health Supportive Supervisor Behaviors (MHSSB; i.e., emotional support, practical support, role modeling, reducing stigma, warning sign recognition, warning sign response) that can be enacted and used by supervisors and managers to protect and promote the mental health of employees. A brief overview of mental health, mental disorders, and workplace mental health is provided. This is followed by the theoretical grounding and introduction of MHSSB. Suggestions for future research and practice follow, all with the focus of developing a better understanding of the role of supervisors in protecting and promoting employee mental health in the workplace.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".