Healthcare professional disclosure of mental illness in the workplace: a rapid scoping review
Bibliographic record
Abstract
BACKGROUND: Although mental health difficulties are common among healthcare professionals (HCP), little research exists exploring the decision to disclose these difficulties in the healthcare context. AIMS: This rapid scoping review aims to explore HCP disclosure of mental health difficulties in the workplace. METHODS: The methodological framework was based on rapid and scoping review guidelines. A thematic synthesis approach was used for data analysis. RESULTS: Seventeen articles were included. Disclosure was found to be a process that starts with weighing its pros ("personal benefits", "personal beliefs", and "professional responsibility") and cons ("fears related to professional identity", "fears related to employment", "risk of stigmatization", and "personal experiences with mental health difficulties"). A decision-making process then occurs to help HCPs figure out how to disclose. Situations of nonconsensual disclosure can transpire through "third party disclosure" or "inadvertent disclosure". Disclosure results in outcomes including "positive experiences", "negative personal consequences" and "negative consequences related to others". CONCLUSION: Disclosure in healthcare and other workplaces is a complex process with few benefits and many potential repercussions. However, there is an opportunity to improve. Recognizing the value of and educating the workforce about HCPs with mental health difficulties will help work environments become safer for disclosure.
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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 | 0.037 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| 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".