Understanding Men’s Experiences With Prostate Cancer Stigma: A Qualitative Study
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to explore the experiences and perspectives of men who have had prostate cancer to better understand the effect of prostate cancer and associated stigmas on men in the Canadian province Newfoundland and Labrador (NL). PARTICIPANTS & SETTING: Eleven men from NL who have had prostate cancer participated in semistructured interviews exploring their perspectives and experiences of prostate cancer and stigma. METHODOLOGIC APPROACH: A social-ecological framework was used to understand experiences from different domains. Interviews were analyzed using Lichtman's three Cs approach. Analysis focused on establishing themes of the participants' lived experience of prostate cancer and related stigma. FINDINGS: Participants described how emasculating a prostate cancer diagnosis can feel. They identified ways prostate cancer negatively affected their behaviors and sense of self, described coping with the diagnosis and different strategies, and talked about broader system change required to address prostate cancer stigma. Participants expressed a need for additional support from healthcare providers (HCPs). IMPLICATIONS FOR NURSING: HCPs, such as oncology nurses, may be able to reduce stigmatization by providing patient navigation, improving information delivery, or providing psychosocial counseling to individuals experiencing feelings of internal or external stigmatization related to prostate cancer.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".