Advocacy, support and survivorship in prostate cancer
Bibliographic record
Abstract
Across Australia, prostate cancer support groups (PCSG) have emerged to fill a gap in psychosocial care for men and their families. However, an understanding of the triggers and influencers of the PCSG movement is absent. We interviewed 21 SG leaders (19 PC survivors, two partners), of whom six also attended a focus group, about motivations, experiences, past and future challenges in founding and leading PCSGs. Thematic analysis identified four global themes: illness experience; enacting a supportive response; forming a national collective and challenges. Leaders described men's feelings of isolation and neglect by the health system as the impetus for PCSGs to form and give/receive mutual help. Negotiating health care systems was an early challenge. National affiliation enabled leaders to build a united voice in the health system and establish a group identity and collective voice. Affiliation was supported by a symbiotic relationship with tensions between independence, affiliation and governance. Future challenges were group sustainability and inclusiveness. Study findings describe how a grassroots PCSG movement arose consistent with an embodied health movement perspective. Health care organisations who seek to leverage these community resources need to be cognisant of SG values and purpose if they are to negotiate effective partnerships that maximise mutual benefit.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 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".