The “room to share”: An ecological perspective on mental health disclosure at work.
Bibliographic record
Abstract
How organizations address employee mental health conditions (MHCs) is an increasingly important topic in occupational health psychology. A key focus of this literature is on understanding how and why employees disclose their MHCs to colleagues. Concealing a stigmatized identity, such as a MHC, can cause distress, while disclosure has been associated with improved well-being and access to proper accommodations. However, employees who disclose a MHC also risk discrimination and mistreatment. Given such competing dynamics, past research has largely framed disclosure through a concerted decision-making lens, where employees weigh the benefits and risks before revealing their condition. Yet the disclosure process can be more complex than these models suggest, with scholars recognizing that no "one-size fits all." To investigate this complexity, we conducted an in-depth narrative interview study with 27 employees living with a MHC. Our findings challenge the assumption that MHC disclosure is typically premeditated. We develop the concept of disclosure opportunities-situations that enable employees to share their MHC at work. We also identify four key elements of the work environment-time and space, bureaucratic structure, social structure, and mental health programs-that shape these opportunities. These elements can either facilitate or constrain disclosure, depending on how they interact. Using these insights, we propose an ecological model of MHC disclosure that complements and extends existing decision-based models, offering a more complex and nuanced understanding of how disclosure unfolds at work. We then explain how this model can inform the practice of occupational health psychology. (PsycInfo Database Record (c) 2025 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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.045 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| 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".