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Record W4392346045 · doi:10.1080/28324765.2024.2313870

A survey of Canadian men’s mental health in the workplace

2024· article· en· W4392346045 on OpenAlexaffabout
Paul Sharp, John L. Oliffe, David Kealy, Simon Rice, Zac E. Seidler, John S. Ogrodniczuk

Bibliographic record

VenueCogent Mental Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMental healthPsychologyApplied psychologyPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.732
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.408
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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