A survey of Canadian men’s mental health in the workplace
Bibliographic record
Abstract
Work plays a central role in many men's lives and can be a major contributor to mental health.The current study aim was to examine the prevalence and nature of Canadian men's mental health challenges at work, including diverse indicators of mental health and workplace characteristics.Participants (N = 451) were men (M = 49.97 years; SD = 14.99) employed in British Columbia, Canada, who were recruited via a market research online panel.Questionnaires were used to collect men's mental health data (e.g.depression, loneliness), work-related health (e.g.burnout, bullying), and workplace performance (e.g.presentism, absenteeism).Findings revealed high rates of hazardous drinking (36%), depression (22%), suicidal/self-injury ideation (18%), and anxiety (14%).Many participants reported that they frequently experienced occupational burnout, and 18% reported that personal problems significantly impaired their work.Of concern, approximately 53% indicated that they keep feelings to themselves and 45% reported that they would prefer not to talk about their problems.Important factors related to mental health symptoms were identified (e.g.age, distress concealment, male-dominated workplace).These findings highlight the need for policy makers and employers to take immediate action to address men's mental health challenges by developing initiatives that promote men's mental health in the workplace.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".