Men’s health in alternative spaces: exploring men’s sheds in Ireland
Bibliographic record
Abstract
Men often struggle to find or gain access to meaningful health services. Despite, or because of this trend, men are increasingly seeking out alternative sources of support for their wellbeing. Global health conversations and policies are calling for a greater awareness of men's heath issues, uptake of gender-sensitive strategies and integration of community and voluntary sectors. Men's Sheds, which are community and volunteer-run spaces for men, are increasing in popularity for men across Ireland. This study aimed to investigate men's experiences as members of Men's Sheds and the relationship between their involvement in the Shed and their wellbeing. Qualitative methods including: semi-structured interviews, focus groups and observations were used with men (n = 27) from five different Sheds across Ireland. Findings suggest that key features of Shed participation (i.e. using and developing new skills, feeling a sense of belonging, supporting and being supported by peers, and contributing to community) contribute to men's overall wellbeing as well as men's buy-in or support for Men's Sheds. Despite support for Men's Sheds, negotiating membership, funding and boundaries of peer support remain persistent challenges that threaten the sustainability of Shed space and membership. Future work that examines opportunities for meaningful collaboration between Sheds and surrounding community services could help provide more pathways for men to access support without compromising the integrity and intentionality of Sheds as peer-run spaces.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".