Evaluating a Tool to Support the Integration of Gender in Programs to Promote Men’s Health
Bibliographic record
Abstract
Men's disproportionate rate of suicide and substance use has been linked to problematic conformity to traditional masculine ideals. Mental health promotion interventions directed toward men should address the gender-specific needs of men; yet, no tools exist to provide such guidance. To address this need, the Check-Mate tool was developed as part of a global evaluation of the Movember Foundation's Social Innovators Challenge (SIC). The tool provides an initial set of evidence-based guidelines for incorporating gender-related influences in men's mental health promotion programs. This article describes the development of Check-Mate and an evaluation of its usability and usefulness. Using a qualitative descriptive approach, semistructured interviews were conducted with the leads for eight of the SIC projects; they used the tool for these. Data were analyzed using conventional content analysis. Overall, project leads found the tool user-friendly. Identified strengths of Check-Mate included its practicality, adaptability, usefulness for priming thinking on gender sensitization, and value in guiding program planning and implementation. With respect to limitations, project leads explained that the complexity of men's mental health promotion programming may limit applicability of some or all approaches included in Check-Mate. They also expressed concern about how using Check-Mate might reinforce hegemonic masculine ideals. It was suggested that examples illustrating the use of Check-Mate would be a helpful accompaniment to the tool. Findings indicate that Check-Mate is a useful guide in men's mental health promotion programming. In addition to future testing of the tool in different settings, links between the tool's approaches and program outcomes should be explored.
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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 | 0.067 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".