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Record W2894045301 · doi:10.1186/s12885-018-4839-y

The BETTER WISE protocol: building on existing tools to improve cancer and chronic disease prevention and screening in primary care for wellness of cancer survivors and patients – a cluster randomized controlled trial embedded in a mixed methods design

2018· article· en· W2894045301 on OpenAlexafffund
Donna Manca, Carolina Fernandes, Eva Grunfeld, Kris Aubrey‐Bassler, Melissa Shea‐Budgell, Aïsha Lofters, Denise Campbell‐Scherer, Nicolette Sopcak, Mary Ann O’Brien, Christopher Meaney, Rahim Moineddin, Kerry McBrien, Ginetta Salvalaggio, Paul Krueger

Bibliographic record

VenueBMC Cancer · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of CalgaryOntario Institute for Cancer ResearchMemorial University of NewfoundlandCovenant HealthUniversity of TorontoGrey Nuns Community HospitalUniversity of Alberta
FundersAlberta InnovatesAlberta Innovates - Health Solutions
KeywordsMedicineRandomized controlled trialPopulationHealth careIntervention (counseling)Family medicineCancer screeningCluster randomised controlled trialPhysical therapyCancerNursingSurgeryEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: There is a pressing need to reduce the burden of chronic disease and improve healthcare system sustainability through improved cancer and chronic disease prevention and screening (CCDPS) in primary care. We aim to create an integrated approach that addresses the needs of the general population and the special concerns of cancer survivors. Building on previous research, we will develop, implement, and test the effectiveness of an approach that proactively targets patients to attend an individualized CCDPS intervention delivered by a Prevention Practitioner (PP). The objective is to determine if patients randomized to receive an individualized PP visit (vs standard care) have improved cancer surveillance and CCDPS outcomes. Implementation frameworks will help identify and address facilitators and barriers to the approach and inform future dissemination and uptake. METHODS/DESIGN: The BETTER WISE project is a pragmatic two-arm cluster randomized controlled trial embedded in a mixed methods design, including a qualitative evaluation and an economic assessment. The intervention, informed by the expanded chronic care model and previous research, will be refined by engaging researchers, practitioners, policy makers, and patients. The BETTER WISE tool kit includes blended care pathways for cancer survivors (breast, colorectal, prostate) and CCDPS including lifestyle risk factors and screening for poverty. Patients aged 40-65, including both cancer survivors and general population patients, will be randomized at the physician level to an intervention group or to a wait-list control group. Once the intervention is completed, patients randomized to wait-list control will be invited to receive a prevention visit. The main outcome, calculated at 12-months follow-up, will be an individual patient-level summary composite index, defined as the proportion of CCDPS actions achieved relative to those for which the patient was eligible at baseline. A qualitative evaluation will capture information related to program outcome, implementation (facilitators and barriers), and sustainability. An economic assessment will examine the projected cost-benefit impact of investing in the BETTER WISE approach. DISCUSSION: This project builds on existing work and engages end users throughout the process to develop, implement, and determine the effectiveness of a multi-faceted intervention that addresses CCDPS and cancer survivorship in primary care settings. TRIAL REGISTRATION: ISRCTN21333761 . Registered on December 19, 2016.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.041
GPT teacher head0.398
Teacher spread0.357 · 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 designRandomized 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

Citations25
Published2018
Admission routes2
Has abstractyes

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