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Record W2198779068 · doi:10.1186/s12885-015-2003-5

Healthy Living after Cancer: a dissemination and implementation study evaluating a telephone-delivered healthy lifestyle program for cancer survivors

2015· article· en· W2198779068 on OpenAlexafffund
Elizabeth Eakin, Sandra C. Hayes, Marion Haas, Marina M. Reeves, Janette L. Vardy, Frances Boyle, Janet E. Hiller, Gita D. Mishra, Ana D. Goode, Michael Jefford, Bogda Koczwara, Christobel Saunders, Wendy Demark‐Wahnefried, Kerry S. Courneya, Kathryn H. Schmitz, Afaf Girgis, Kate White, Kathy Chapman, Anna Boltong, Katherine Lane, Sandy McKiernan, Lesley Millar, Lorna O’Brien, Greg Sharplin, Polly Baldwin, Erin Robson

Bibliographic record

VenueBMC Cancer · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Alberta
FundersCancer Council QueenslandCancer Council VictoriaCancer Council NSWNational Health and Medical Research CouncilAustralian Research CouncilMedical Research CouncilFriends of The Mater FoundationIngham Institute for Applied Medical ResearchCanada Research ChairsPerelman School of Medicine, University of PennsylvaniaSwinburne University of TechnologyUniversity of AlbertaNational Breast Cancer FoundationUniversity of New South WalesPeter MacCallum Cancer CentreUniversity of Technology SydneyUniversity of SydneyUniversity of PennsylvaniaCancer Institute NSWMater Foundation
KeywordsMedicineCancer survivorHealth coachingCancerPsychosocialPsychological interventionSurvivorship curveCoachingGerontologyCancer survivorshipFamily medicineCancer preventionPhysical therapyIntervention (counseling)NursingPsychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Given evidence shows physical activity, a healthful diet and weight management can improve cancer outcomes and reduce chronic disease risk, the major cancer organisations and health authorities have endorsed related guidelines for cancer survivors. Despite these, and a growing evidence base on effective lifestyle interventions, there is limited uptake into survivorship care. METHODS/DESIGN: Healthy Living after Cancer (HLaC) is a national dissemination and implementation study that will evaluate the integration of an evidence-based lifestyle intervention for cancer survivors into an existing telephone cancer information and support service delivered by Australian state-based Cancer Councils. Eligible participants (adults having completed cancer treatment with curative intent) will receive 12 health coaching calls over 6 months from Cancer Council nurses/allied health professionals targeting national guidelines for physical activity, healthy eating and weight control. Using the RE-AIM evaluation framework, primary outcomes are service-level indicators of program reach, adoption, implementation/costs and maintenance, with secondary (effectiveness) outcomes of patient-reported anthropometric, behavioural and psychosocial variables collected at pre- and post-program completion. The total participant accrual target across four participating Cancer Councils is 900 over 3 years. DISCUSSION: The national scope of the project and broad inclusion of cancer survivors, alongside evaluation of service-level indicators, associated costs and patient-reported outcomes, will provide the necessary practice-based evidence needed to inform future allocation of resources to support healthy living among cancer survivors. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry (ANZCTR)--ACTRN12615000882527 (registered on 24/08/2015).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.059
GPT teacher head0.459
Teacher spread0.400 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations77
Published2015
Admission routes2
Has abstractyes

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