Can lifestyle interventions improve Canadian men’s mental health? Outcomes from the HAT TRICK programme
Bibliographic record
Abstract
Engaging men in mental health promotion can be difficult because of reticence about help-seeking, especially for gender neutral programmes. Developments in men's health research has pointed to the success of gender-sensitized programmes to increase men's engagement in healthy lifestyle interventions targeting physical activity and healthy eating; however, less is known about the impact of these interventions on men's mental health. This study explored changes to men's depression risk and health-related quality of life at post-intervention (12 weeks) and 9-month follow-up, after participating in HAT TRICK, a gender-sensitized lifestyle intervention for overweight men. Participants completed validated self-report measures of mental health at baseline, post-intervention (12 weeks) and 9-month follow-up. Men's scores on the Male Depression Risk Scale (MDRS) and the SF-12 questionnaire, including physical health (PH12) and mental health (MH12) composite scores, were analyzed using mixed linear models to assess linear trends. At baseline, men (N = 62) had a mean age of 50.98 (SD = 10.09) years and BMI of 35.87 (SD = 5.51) kg/m2. Results show that both the MDRS and the MH12 showed improvements in participants' mental health, with significant linear trends (p = 0.003; p = 0.003) qualified with significant quadratic trends over time (p = 0.02; p = 0.03). There were no significant changes in the PH12 over time. Gender-sensitized programmes for overweight men, such as HAT TRICK, are a promising approach to positively influence components of men's mental health, with the potential for sustained improvements over the long term.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".