Facilitating Mental Health Disclosure and Better Work Outcomes: The Role of Organizational Support for Disclosing Mental Health Concerns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it