Disclosing a mental health condition in a new job: The critical role of the work environment.
Bibliographic record
Abstract
OBJECTIVE: This cross-sectional study aimed to identify personal, relational, and organizational factors associated with disclosing (or not) mental disorders to supervisors in a new job. Disclosing a mental health condition is often essential for obtaining work accommodations and enhancing job retention. Decision to not disclose is usually associated with fear of stigma and discrimination. METHODS: Participants from Quebec and Ontario (Canada) who had recently obtained employment in the competitive labor market after experiencing unemployment due to a mental disorder were recruited through online advertisements and supported employment services. Questionnaires were administered to assess personal, relational, and organizational factors. Logistic regression analyses were performed to examine associations between these factors and the decision to disclose a mental health condition. RESULTS: < .001) as significant factors positively associated with disclosure of a mental health condition to immediate supervisors. These findings suggest that individuals with greater decision-making latitude in their jobs, and those who received support from their coworkers were more likely to disclose their mental health condition to their supervisor. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: A supportive work environment plays a critical role in facilitating disclosure in the workplace. More longitudinal studies are needed to better understand the impact of these relationships on job tenure long term. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".