“Things I did not know”: Retrospectives on a Canadian rural male youth suicide using an instrumental photovoice case study
Bibliographic record
Abstract
In Canada, it is young, rural-based men who are at the greatest risk of suicide. While there is no consensus on the reasons for this, evidence points to contextual social factors including isolation, lack of confidential services, and pressure to uphold restrictive norms of rural masculinity. In this article, we share findings drawn from an instrumental photovoice case study to distil factors contributing to the suicide of a young, Canadian, rural-based man. Integrating photovoice methods and in-depth qualitative, we conducted interviews with seven family members and close friends of the deceased. The interviews and image data were analyzed using constant comparative methods to discern themes related to participants' reflections on and perceptions about rural male suicide. Three inductively derived themes, "Missing the signs," "Living up to his public image," and "Down in Rural Canada," reflect the challenges that survivors and young rural men can experience in attempting to be comply with restrictive dominant ideals of masculinity. We conclude that community-based suicide prevention efforts would benefit from gender-sensitive and place-specific approaches to advancing men's mental health by making tangibly available and affirming an array of masculinities to foster the well-being of young, rural-based men.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".