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Record W3080384968 · doi:10.1188/20.onf.577-585

Understanding Men’s Experiences With Prostate Cancer Stigma: A Qualitative Study

2020· article· en· W3080384968 on OpenAlexaffabout
Richard Buote, Erin Cameron, Ryan Collins, Erin McGowan

Bibliographic record

VenueOncology nursing forum · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsNOSM UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineProstate cancerAnxietyDepression (economics)GynecologyQualitative researchCancerClinical psychologyGerontologyFamily medicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

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.

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

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.129
GPT teacher head0.418
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2020
Admission routes2
Has abstractyes

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