Older men's perceptions of the need for and access to male-focused community programmes such as Men's Sheds
Bibliographic record
Abstract
Although participating in community social programming is associated with positive physical and mental health outcomes for older adults, older men participate less often than women. Men's Sheds is a community programme used primarily by older men that originated in Australia and is well established there. The goal of the current study was to explore men's perceptions of the need for Men's Sheds and issues concerning access to them in Canada, a country with a small but growing Men's Sheds movement. We conducted focus groups with 64 men aged 55 years and older, including Men's Sheds members and men from the community who were unfamiliar with this programme, and analysed the data using the framework analytic approach. The data revealed two primary themes concerning: (a) the need for male-focused community programmes, including the sub-themes reducing isolation, forming friendships and engaging in continued learning; and (b) access to programmes, including the sub-themes points of contact, sustaining attendance and barriers. Findings suggest that in order to reduce the likelihood of isolation and increase opportunities for social engagement, exposure to the concept of male-focused programming should begin before retirement age. In addition, such programmes should be mindful of how they are branded and marketed in order to create spaces that are welcoming to new and diverse members.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".