Sometimes You Need More than a Wingman: Masculinity, Femininity, and the Role of Humor in Men's Mental Health Help-Seeking Campaigns
Bibliographic record
Abstract
The clinical literature has consistently documented that men seek help for mental health less often than do women, although they suffer from mental illness at comparable rates. This is particularly troublesome as depression and anxiety in men are more likely to manifest in substance abuse and suicidal behavior. This gender discrepancy in help-seeking may be explained by the social psychological literature on traditional masculinity, which has been associated with stigmatizing thoughts about mental illness and opposition to help-seeking. The present research explored this link between masculinity and mental health help-seeking, including the use of affiliative humor in public awareness messages about help-seeking for mental health. We hypothesized that incorporating light humor into this campaign might reframe help-seeking in a less threatening way, effectively circumventing the defensive reactions of masculine men. Across three studies, we presented young men with ads encouraging them to reach out to a friend suffering from anxiety or depression. Consistently, the perceived funniness of the ads predicted their persuasiveness without increasing stigma or trivializing the issue of mental health. Masculinity did not in fact predict stigmatizing and defensive thoughts about mental illness; rather, men's femininity emerged as the strongest and most consistent predictor of these reactions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".