Examples of mental health campaigns targeted at men
Bibliographic record
Abstract
Men commit suicide more frequently than women in nearly all parts of the world. This is due to a number of factors, including the fact that women tend to seek mental help relatively more often than men. Many developed countries have attempted to implement suicide prevention programmes and initiatives, but only a small proportion of these have targeted men directly. The aims of this paper are to highlight preventive measures focusing on mental problems experienced specifically by men, and provide a detailed description of these measures. Consequently, the paper has both theoretical and practical dimensions, outlining the challenges and presenting potential solutions. The study discusses selected examples of mental health actions targeting men which were carried out in several countries around the world including Japan (“Daddy, have you slept well?”), UK (“Heads Up” campaign), United States (“Real men. Real depression”), Canada (“HeadsUpGuys” campaign), and Australia (Movember), as well as social media campaigns (Instagram: #HereForYou). Men are often reluctant to reveal their mental problems (stress, low mood, insomnia, suicidal ideation), which can be attributed to the so-called macho culture existing in societies. The man is traditionally considered head of the family, a guardian and leader, a strong personality, and a person to rely on. This perception contributes to the fact that men tend not to admit their weaknesses in front of family or friends. It is recommended that preventive actions take into account such aspects as the transfer of knowledge, skills and competences in the areas of identification of mental problems, and abilities to cope with a crisis situation. Mental health actions should be launched in areas frequently visited by men including workplaces, buses, pubs, bars, sports clubs, stadiums or other meeting places, so that the information can reach the largest possible target group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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; 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".