Facilitating Mental Health Disclosure and Better Work Outcomes: The Role of Organizational Support for Disclosing Mental Health Concerns
Bibliographic record
Abstract
ABSTRACT Mental health concerns among employees are increasingly prevalent, yet many employees remain under‐supported. Disclosure is a critical step in accessing organizational support for mental health. Drawing on social information processing theory, we introduce the concept of organizational support for disclosing mental health concerns and develop a scale assessing three dimensions: absence of anticipated discrimination and stigma, availability of organizational resources, and presence of social support. Across two studies, we show that organizational support for disclosing mental health concerns is positively associated with employees' willingness to disclose and actual disclosure behaviors. Greater organizational support for disclosing mental health concerns is also linked to reduced mental health challenges (e.g., lower anxiety and depression) and improved work outcomes, including higher work engagement, job satisfaction, and organizational citizenship behavior, alongside lower turnover intentions and absenteeism. Our findings provide a framework for assessing employees' perceptions of disclosure support and offer practical insights for HR professionals seeking to foster disclosure‐friendly work environments. Finally, we contribute to the debate on mandatory disability reporting by identifying organizational factors that can enhance disclosure rates and improve support for employees with mental health concerns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".