Young Workers' Responses to Mental Ill-Health in the Workplace
Bibliographic record
Abstract
Young adults between the ages of 18 and 25 have the highest prevalence of mental ill-health in North America, which typically coincides with their career launches (National Institute of Mental Health, 2023; Substance Abuse and Mental Health Services Administration, 2023). Mental ill-health represents a state of severe functional impairment in everyday life, where an individual may qualify for a clinical diagnosis and their ability to work may be impacted (Kelloway et al., 2023; Keyes, 2002). The purpose of this research was to explore how young workers, at the beginning of their careers, describe their experiences with mental ill-health at work, how they respond to those challenges, and what that means for their work. Fifteen participants who self-identified as having mental ill-health in the workplace, either post-graduation or approaching graduation, were interviewed using a semi- structured protocol. Using reflexive thematic analysis, I identified four key themes. Most participants' mental ill-health tipping point stemmed from internal, external, and work-related factors. Participants had diverse decision-making processes for responding to mental ill-health at work, with most choosing to stay despite their mental health challenges. Notably, all participants — whether they stayed, quit, or took a leave of absence —reported mental health improvements attributed to learning to transition into the workforce, mitigate harmful workplace stressors, and cultivate realistic workplace expectations.
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How this classification was reachedexpand
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".