Men’s Mental Health: Social Determinants and Implications for Services
Bibliographic record
Abstract
Numerous scholars have stated that there is a silent crisis in men's mental health. In this article, we aim to provide an overview of core issues in the field of men's mental health, including a discussion of key social determinants as well as implications for mental health services. Firstly, we review the basic epidemiology of mental disorders with a high incidence and prevalence in men, including suicide and substance use disorder. Secondly, we examine controversies around the low reported rates of depression in men, discussing possible measurement and reporting biases. Thirdly, we explore common risk factors and social determinants that may explain higher rates of certain mental health outcomes in men. This includes a discussion of 1) occupational and employment issues; 2) family issues and divorce; 3) adverse childhood experience; and 4) other life transitions, notably parenthood. Fourthly, we document and analyze low rates of mental health service utilization in men. This includes a consideration of the role of dominant notions of masculinity (such as stubbornness and self-reliance) in deterring service utilization. Fifthly, we note that some discourse on the role of masculinity contains much "victim blaming," often adopting a reproachful deficit-based model. We argue that this can deflect attention away from social determinants as well as issues within the mental health system, such as claims that it is "feminized" and unresponsive to men's needs. We conclude by calling for a multipronged public health-inspired approach to improve men's mental health, involving concerted action at the individual, health services, and societal levels.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".