Men on Losing a Male to Suicide: A Gender Analysis
Bibliographic record
Abstract
Although male suicide has received research attention, the gendered experiences of men bereaved by male suicide are poorly understood. Addressing this knowledge gap, we share findings drawn from a photovoice study of Canadian-based men who had lost a male friend, partner, or family member to suicide. Two categories depicting the men's overall account of the suicide were inductively derived: (a) unforeseen suicide and (b) rationalized suicide. The "unforeseen suicides" referred to deaths that occurred without warning wherein participants spoke to tensions between having no idea that the deceased was at risk while reflecting on what they might have done to prevent the suicide. In contrast, "rationalized suicides" detailed an array of preexisting risk factors including mental illness and/or substance overuse to discuss cause-effect scenarios. Commonalities in unforeseen and rationalized suicides are discussed in the overarching theme, "managing emotions" whereby participants distanced themselves, but also drew meaning from the suicide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".