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Record W4404759479 · doi:10.1186/s13063-024-08633-5

Implementation and evaluation of a navigation program for people with cancer in old age and their family caregivers: study protocol for the EU NAVIGATE International Pragmatic Randomized Controlled Trial

2024· article· en· W4404759479 on OpenAlexaff
Tinne Smets, Lara Pivodic, Rose Miranda, Fien Van Campe, Chelsea Vinckier, Barbara Pesut, Wendy Duggleby, Andrew Davies, Amanda Lavan, Peter May, Bárbara Gomes, Maja de Brito, Vítor Rodrigues, Katarzyna Szczerbińska, Violetta Kijowska, Ilona Barańska, Stefanie De Buyser, Davide Ferraris, Sara Alfieri, Bianca Scacciati, Helen Cheyne, Kenneth Chambaere, Joni Gilissen, Annicka G. M. van der Plas, Roeline H. R. W. Pasman, Bregje D. Onwuteaka‐Philipsen, Lore Decoster, Lise Rosquin, Síofra Hearne, Małgorzata Filipińska, Adrianna Ziuziakowska, Natalia Drapała, Iris Beijer Veenman, Inês Correia, Sónia Silva, Nele Van Den Noortgate, Eline Naert, Charlèss Dupont, Else Gien Statema, Gloria Puurveen, Monica Gandelli, Laura Gangeri, Lieve Van den Block

Bibliographic record

VenueTrials · 2024
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity of AlbertaUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsRandomized controlled trialMedicineIntervention (counseling)Quality of life (healthcare)Protocol (science)RandomizationPalliative careHealth careNursingFamily medicineGerontologyAlternative medicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Cancer navigation programs aim to support, educate, and empower patients and families, addressing barriers to diagnostics, treatment, and care. Navigators engage with people to ensure timely access to services and resources. While promising for older people with cancer, these programs are scarce in Europe, and research on their effectiveness and implementation is limited. We describe the protocol of the EU NAVIGATE randomized controlled trial, aimed to evaluate (1) effectiveness and cost-effectiveness of NavCare-EU, an intervention that aims to support older people with cancer throughout their illness trajectory, spanning the continuum of supportive, palliative, and end-of-life care, and (2) the intervention's implementation processes and feasibility of its integration into different health care systems in Europe, contextual barriers and facilitators for effective and sustainable implementation, and mechanisms involved in reaching the outcomes. METHODS: We will conduct a multisite pragmatic fast-track randomized controlled trial with embedded convergent mixed-method process evaluation in Belgium, Ireland, Italy, Netherlands, Poland, and Portugal. The study targets people with cancer and declining health, 70 years or older, and their close family caregivers. The trial compares the NavCare-EU intervention plus standard care with standard care alone. We will perform a baseline measurement prior to randomization and follow-up measurements at 12 weeks, 24 weeks, and 48 weeks in intervention and control group, and an additional measurement at 72 weeks in the control group. Primary outcomes, measured at 24 weeks are (1) the older person's global health status/quality of life, a 2-item subscale from EORTC-QLQ-C30 (revised) measuring health-related quality of life, (2) level of social support measured with Medical Outcomes Study Social Support Survey (MOS-SSS scale). The study will include at least 246 older persons with completed global health status/quality of life at 24 weeks. DISCUSSION: The EU NAVIGATE trial will cross-nationally test the effectiveness and cost-effectiveness of a navigation intervention for older people with cancer and their family caregivers, and its implementation in different health care systems in Europe. As continuity and access to health, social, and community care is a priority for patients and caregivers, the trial is timely and critically needed. TRIAL REGISTRATION: Clinicaltrials.gov: identifier NCT06110312 (2023/10/31).

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)0.011
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.103
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
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 head0.091
GPT teacher head0.489
Teacher spread0.398 · 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
GenreProtocol

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

Citations5
Published2024
Admission routes1
Has abstractyes

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