Bibliographic record
Abstract
Investigating the impact of gender on mental health is an enduring tradition in psychiatric research. Much of this has focused on women’s mental health, examining the social exposures that influence the onset and course of mental disorders with a high prevalence in women. This includes classic work exploring the role of chronic and acute stress in the aetiology of depression in women1,2 and research on the relationship between media consumption and eating disorders.3,4 In contrast, a definable field of men’s mental health has only emerged in the last decade, and even then, this has been a quiet emergence.5,6 This developing field is based on epidemiological findings that men experience much higher rates of certain mental health outcomes in comparison to women. For example, research consistently shows than men make up around 75% to 80% of all completed suicides in Canada and other western countries.7,8 Likewise, rates of substance use disorder are significantly elevated in men, with around 3 out of 4 cases being male.9 Moreover, men still tend to under-utilize mental health services, with figures indicating that only around 30% of people who use mental health services are men.10 All this is instantiated in recent years through examination of statistics related to Canada’s ongoing fentanyl crisis. Statistics released by the B.C Coroner’s office indicate that over 80% of deaths in this crisis are male, mirroring statistics in other jurisdictions.11 This has led researchers, commentators, and journalists to describe men’s mental health issues (and male suicide in particular) in ominous terminology: a ‘silent epidemic’,12 a ‘quiet catastrophe’,13 ‘a gender gap that is a matter of life and death’14 and ‘an invisible crisis’.15 Given this situation, this in review series attempts to amplify the discussion about men’s mental health, illuminating concepts and underlying issues. The 2 papers that make up this in review series advance the literature on men’s mental health. The first paper, by Affleck et al., investigates the social determinants of men’s mental health, and implications for mental health services. The second paper, by Bilsker et al., outlines critical issues in men’s mental health, examining clinical interventions and population-health initiatives to tackle underlying issues. Although each paper touches on unique concerns, it is interesting to note that both papers 1) adopt a public-health approach to the underlying issues, moving beyond the common tendency to ‘victim-blame’ men who have mental health issues; 2) emphasize the importance of documenting and addressing the oft-ignored social determinants of men’s mental health; and 3) encourage critical reflection on the configuration and nature of mental health services vis-a-vis men’s mental health.
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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".