Mental Health in Organizations: Theoretical Analysis and Proposal of the Author’s Concept of STEP-Method of Supporting Mental Well-Being of Employees
Bibliographic record
Abstract
This article examines the theoretical underpinnings of mental health in organizations and proposes the author’s STEP Method—Strategy, Training, Engagement, and Prevention—as an integrated framework for promoting employee well-being. The discussion begins with an overview of contemporary definitions and classifications of mental health, highlighting the continuum from flourishing to diagnosed mental disorders. It then addresses key organizational factors such as leadership style, stigma reduction, and psychosocial interventions, underscoring the impact of mental health on absenteeism, presenteeism, and turnover. Drawing on current research and best practices, the article demonstrates how the STEP Method situates mental health initiatives within a broader corporate strategy. By embedding training modules for both leaders and employees, fostering continuous engagement, and prioritizing prevention, STEP offers a holistic approach that extends beyond traditional individual-level solutions (eg, Employee Assistance Programs). It is argued that this structured, systemic orientation not only mitigates work-related stressors but also cultivates a supportive culture in which employees are empowered to seek help proactively. The author concludes that STEP may serve as a cornerstone for building resilient, adaptable organizations well-positioned to address evolving mental health challenges.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.006 | 0.006 |
| 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".