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Record W2995960664 · doi:10.1188/20.onf.33-43

Nurse-Led Supportive Care Intervention for Men With Advanced Prostate Cancer: Healthcare Professionals' Perspectives

2019· article· en· W2995960664 on OpenAlexaff
Nicholas Ralph, Suzanne K. Chambers, Kirstyn Laurie, John L. Oliffe, Mark Lazenby, Jeff Dunn

Bibliographic record

VenueOncology nursing forum · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineNursingIntervention (counseling)Prostate cancerHealth professionalsHealth careOncology nursingFamily medicineCancerNurse educationInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To identify barriers and corresponding solutions for implementing a telephone-based, nurse-led supportive care intervention for men with advanced prostate cancer. PARTICIPANTS & SETTING: 21 healthcare professionals with an average 15.81 years of experience in diverse prostate cancer care settings. METHODOLOGIC APPROACH: Data from semistructured interviews were coded into the Theoretical Domains Framework and mapped to behavior change techniques (BCTs) to inform the development of an implementation schema. FINDINGS: Barriers included lack of knowledge about the effectiveness of survivorship interventions and how to deliver them, low referral rates to psychosocial oncology care, low help-seeking behavior among men with advanced prostate cancer, lack of care coordination skills, and inadequate service capacity. IMPLICATIONS FOR NURSING: Interprofessional support exists for a nurse-led supportive care intervention. Causes of low engagement with supportive care among men with advanced prostate cancer extend beyond gendered patterns of response.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.371
Teacher spread0.363 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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