Connecting humor, health, and masculinities at prostate cancer support groups
Bibliographic record
Abstract
OBJECTIVE: Many commentaries about men's health practices and masculinities indicate that men do not typically engage with self-health or acknowledge illness, let alone openly discuss their health concerns with other men. Prostate cancer support groups (PCSGs) appear to run contrary to such ideals, yet the factors that influence men's attendance and engagement at group meetings are poorly understood. As part of a larger PCSG study, we noticed that humor was central to many group interactions and this prompted us to examine the connections between humor, health, and masculinities. METHODS: A qualitative ethnographic design was used to direct fieldwork and conduct participant observations at the meetings of 16 PCSGs in British Columbia, Canada. Individual semi-structured interviews were completed with 54 men who attended PCSGs to better understand their perceptions about the use of humor at group meetings. RESULTS: Four themes, disarming stoicism, marking the boundaries, rekindling and reformulating men's sexuality, and when humor goes south were drawn from the analyses. Overall, humor was used to promote inclusiveness, mark the boundaries for providing and receiving mutual help, and develop masculine group norms around men's sexuality. Although there were many benefits to humor there were also some instances when well-intended banter caused discomfort for attendees. CONCLUSIONS: The importance of group leadership was central to preserving the benefits of humor, and the specificities of how humor is used at PCSGs may provide direction for clinical practice and the design of future community-based men's health promotion programs.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".