Bibliographic record
Abstract
Purpose The purpose of this paper is to explore recent research on reducing suicide, especially in men, who are often seen as excluding themselves from needing support, or they are excluded because people think they do not want it. Design/methodology/approach A search was carried out for recent papers on suicide prevention in men. Findings One study of 75 regions of Europe reported a link between higher value on giving social support and lower suicide rates, especially for men. Another study reported on the fall in a previously high suicide rate, especially in men, in Quebec province in Canada. A programme of suicide prevention may have contributed to this reduction. Finally, a small interview study reported on how certain kinds of encounters with professionals can inspire hope to carry on after a suicide attempt. Originality/value The two papers looking at regions (across Europe and one province of Canada) suggest how social forces may contribute to reducing suicide, especially in men. The Canadian study suggests the possibility that suicide might be reduced partly by enabling help-seeking in men to be seen as a positive aspect of masculine identity, rather than seeing masculinity as excluding men from support. The small qualitative study illustrates vividly how individual encounters after a suicide attempt might promote hopefulness and are relevant to both sexes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 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; 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".