Invisible Burdens: Mental Health as an Underlying Factor Shaping Leadership Emergence and Disclosure
Bibliographic record
Abstract
Implicit Leadership Theory (ILT) posits that individuals hold cognitive prototypes about who leaders should be and how they should behave, emphasizing traits such as competence and decisiveness (Epitropaki & Martin, 2004). Recent research has extended ILT to explore leadership mental health expectations, revealing that leaders are often expected to embody good mental health (Cloutier & Barling, 2023). These expectations can significantly influence leadership emergence and mental health disclosure, yet their implications remain underexplored. This symposium investigates how mental health expectations shape leader emergence and disclosure decisions. The first three presentations explore barriers to leadership pursuit, focusing on the reluctance to lead due to perceived well-being risks, the role of mental health in shaping self-perceptions of leader suitability, and the impact of internalized mental illness stigma on leadership efficacy. The final two presentations address the challenges of mental health disclosure in leadership roles, examining how leaders’ willingness to disclose is influenced by role identity and perceived risks, and the organizational consequences of such disclosures. Collectively, these studies underscore the complex interplay between leadership expectations, mental health, and organizational dynamics, offering insights into promoting inclusive and supportive leadership environments. Well-being Factors Impacting Reluctance to Lead Author: Cindy D. Suurd Ralph; Royal Military College of Canada Author: Iris Kinnon; - Employee Mental Health and Leadership Pursuit: An Identity Perspective Author: Anika Cloutier; Author: Alyson Byrne; Memorial University Author: Julian Barling; Queen's University Internalization of Mental Illness Stereotypes as a Barrier to Leadership Emergence Author: Michaela Scanlon; Queen's University Author: Julian Barling; Queen's University Leading in Silence: Mental Health Disclosure Among Leaders vs. Followers Author: Maria Angelina Adams; Dalhousie University Author: Anika Cloutier; Leader Self-Disclosure of Mental Illness History Encourages Employees to Access MH Resources Author: Amanda J. Hancock; Author: Ana Askari; University of Regina Author: Kara Anne Arnold; Memorial University
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".