Effectiveness of man therapy to reduce suicidal ideation and depression among working‐age men: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: This randomized controlled trial of the online intervention, man therapy (MT), evaluated efficacy to reduce suicidal ideation (SI) and depression among working-aged men. METHOD: Five-hundred and fifty-four men enrolled and 421 completed all surveys. Control Condition men explored the Healthy Men Michigan (HMM) website and Intervention Condition men explored HMM and MT. Hypotheses included men who used MT would report decreased SI and depression over time compared to Control Condition men. RESULTS: Latent growth curve modeling revealed improvements in SI (slope = -0.23, p < 0.001, 95% CI: -0.29, -0.16) and depression (slope = -0.21, p < 0.001, 95% CI: -0.23, -0.18) over time for men in both groups; however, there was no difference in slope based on group assignment. Depression, lifetime suicide attempts, and interpersonal needs were associated with SI. Interpersonal needs and poor mental health were associated with depression. No group differences in change in risk and protective factors over time were observed. MT sub-group analyses revealed significant improvements in risk and protective factors. CONCLUSION: While a direct effect of MT versus HMM on SI or depression was not observed, men in both groups improved. Results suggest online screening might play a role in reducing SI and depression among men and there are potential benefits to MT related to mental health, social support, and treatment motivation.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".