“To Be a Guy Is to Be Human”: Outcomes of the WiseGuyz Program Through Photo-Based Evaluation
Bibliographic record
Abstract
Gender-transformative approaches (i.e., approaches that support male-identified individuals to critique and resist stereotypical male gender role norms that negatively affect health and well-being) are increasingly recognized as a key health promotion strategy. However, there is limited evidence to date on gender-transformative interventions for male-identified adolescents. In addition, given the dynamic and socially constructed nature of gender, methods beyond quantitative data collection are needed to gain a holistic understanding of promising gender-transformative health promotion approaches. One newer method to capture lived experiences with adolescents is photo-based evaluation, where youth program participants take pictures to represent their knowledge, attitudes, and/or behaviors before and after a program. The present study presents findings from the photo-based evaluation of a gender-transformative health promotion program called WiseGuyz. WiseGuyz is offered to mid-adolescent, male-identified youth in school and community settings, and is designed to promote mental and sexual health and prevent violence. Six youth photographers from a rural Canadian setting took part in this evaluation, taking photos to represent what being a guy in their world meant before and after WiseGuyz. Youth then participated in an individual visual storytelling interview and a group-based photovoice process. Key themes in relation to masculinities that emerged from these data were around changes to (1) social norms and (2) emotionality following program participation, and the need for a safe program space to support these changes. This study adds to literature demonstrating the promise of gender-transformative approaches with adolescents, with implications for future health promotion research and practice with male-identified youth.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".