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Record W2072585783 · doi:10.1177/1557988314543510

Prostate Cancer Support Groups

2014· article· en· W2072585783 on OpenAlexafffundabout
John L. Oliffe, Suzanne K. Chambers, Bernie Garrett, Joan L. Bottorff, Michael McKenzie, Christina Han, John S. Ogrodniczuk

Bibliographic record

VenueAmerican Journal of Men s Health · 2014
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsBC Cancer AgencyUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersCanadian Institutes of Health ResearchCanadian Urological Association
KeywordsProstate cancerPsychosocialMedicineFamily medicinePsychologyCancerMedical educationInternal medicine

Abstract

fetched live from OpenAlex

To understand prostate cancer (PCa) specialists' views about prostate cancer support groups (PCSGs), a volunteer sample of Canada-based PCa specialists (n = 150), including urologists (n = 100), radiation oncologists (n = 40), and medical oncologists (n = 10) were surveyed. The 56-item questionnaire used in this study included six sets of attitudinal items to measure prostate cancer specialists' beliefs about positive and negative influences of PCSGs, reasons for attending PCSGs, the attributes of effective PCSGs, and the value of face-to-face and web-based PCSGs. In addition, an open-ended question was included to invite additional input from participants. Results showed that PCSGs were positively valued, particularly for information sharing, education and psychosocial support. Inclusivity, privacy, and accessibility were identified as potential barriers, and recommendations were made for better marketing PCSGs to increase engagement. Findings suggest prostate cancer specialists highly valued the role and potential benefits of face-to-face PCSGs. Information provision and an educational role were perceived as key benefits. Some concerns were expressed about the ability of web-based PCSGs to effectively engage and educate men who experience 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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.322
Teacher spread0.311 · 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 designOther design
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

Citations16
Published2014
Admission routes3
Has abstractyes

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