Employee Mental Health, Interpersonal Discrimination at Work, and How Human Resources Can Help
Bibliographic record
Abstract
Poor mental health, such as depression, anxiety disorder, and substance abuse disorder, is a common health impairment across the world. For example, since 2019, 50 million adults in the U.S. have experienced poor mental health, and in Canada, approximately 500,000 working adults are unfit to work each week due to poor mental health. Given the prevalence and costs associated with mental health conditions, there is an increasing need to better understand the work experiences of employees experiencing poor mental health. The papers in our symposium join the bourgeoning body of research studying mental health, with an emphasis on the interpersonal dynamics that surround mental health at work. Each considers various forms of interpersonal or subtle discrimination employees might encounter at work as a result of stigma towards mental health. Together the papers represent different lens, including how leaders respond to their employees’ disclosure of a mental health identity, how employees respond to their leaders’ poor mental health, and how employees’ respond to coworkers’ mental health accommodations. The symposium will conclude on a more positive note with a discussion around what organizations can do to help alleviate some of these detrimental interpersonal dynamics. Under Pressure: Exploring Mental Health Disclosures and Supervisor Reactions Author: Ekonkar Kaur; U. of Washington, Seattle Author: Ryan Fehr; U. of Washington, Seattle Employees’ Perceptions of Fairness towards Coworkers’ Accommodations for Mental Health Conditions Author: Daniel James Quintal-Curcic; Telfer School of Management, U. of Ottawa Author: Jane O'Reilly; Telfer School of Management, U. of Ottawa Author: Laurent Lapierre; Telfer School of Management, U. of Ottawa Author: Silvia Bonaccio; Telfer School of Management, U. of Ottawa Mental Illness Stigma Towards Leaders: Consequences for Followers’ Motivation and Performance Author: Michaela Scanlon; Smith School of Business, Queen's U. Author: Julian Barling; Queen's U. Senior Leaders’ Perceptions of the National Standard for Psychological Health and Safety at Work Author: Amanda J. Hancock; U. of Regina Author: Kara Anne Arnold; Memorial U. of Newfoundland Author: Ivy Bourgeault; U. of Ottawa
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".