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Record W2584906582 · doi:10.1111/ecc.12644

Advocacy, support and survivorship in prostate cancer

2017· article· en· W2584906582 on OpenAlexaff
Jeff Dunn, C. Casey, David Sandoe, Melissa K. Hyde, M‐C. Cheron‐Sauer, A. Lowe, John L. Oliffe, Suzanne K. Chambers

Bibliographic record

VenueEuropean Journal of Cancer Care · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsUniversity of British Columbia
FundersAustralian Research CouncilProstate Cancer Foundation of AustraliaProstate Cancer Foundation
KeywordsGrassrootsMedicineFocus groupPsychosocialThematic analysisNursingNegotiationPublic relationsQualitative researchPolitical scienceSociologyPsychiatry

Abstract

fetched live from OpenAlex

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.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.032
GPT teacher head0.340
Teacher spread0.308 · 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 designObservational
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

Citations20
Published2017
Admission routes1
Has abstractyes

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