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Record W4414552111 · doi:10.3389/fonc.2025.1566489

Multimodal group-based tele-prehabilitation for cancer patients and caregivers: a pragmatic multicentre hybrid implementation-effectiveness study protocol

2025· article· en· W4414552111 on OpenAlexaffabout
Isabelle Doré, Alexia Piché, Corentin Montiel, Sylvie Lambert, Chelsia Gillis, Sébastien S. Dufresne, Éléonor Riesco, P. Jardel, Michel Pavic, Vanessa Samouëlian, Samuel Dubé, Isabelle Brisson, Danielle Charpentier

Bibliographic record

VenueFrontiers in Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsShared Services CanadaCégep de ChicoutimiUniversité de SherbrookeUniversité du Québec à ChicoutimiSt Mary's Hospital CentreCentre Hospitalier Universitaire de SherbrookeMcGill University Health CentreUniversité de MontréalSt. Mary's UniversityMcGill UniversityCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsCancerProtocol (science)Intervention (counseling)DisseminationPresentation (obstetrics)Cancer treatmentMEDLINE

Abstract

fetched live from OpenAlex

Background: Multimodal prehabilitation can optimize the physical and psychological health of cancer patients, reduce treatment side effects, hospital stay, and accelerate recovery. The support provided by caregivers reduces the demands on the health care system and can be key in the uptake and maintenance of healthy lifestyle behaviours. However, caregivers support comes at a high cost to their own health. Physical activity can help caregivers maintain their health at the level required to successfully perform their vital roles. Our team has designed the first group-based multimodal tele-prehabilitation program targeting both patients and caregivers: coACTIF. This paper presents the protocol of this implementation-effectiveness study. Methods: This pragmatic, multicentre, hybrid implementation-effectiveness study uses a pre-post-follow-up mixed methods convergent parallel design. The prehabilitation program implementation and effectiveness will be tested in three cities of various sizes in Quebec, Canada. The prehabilitation program includes a virtual supervised group-based exercise program and a web-based educational platform providing learning opportunities and resources on healthy lifestyles and self-management strategies. The study aims to recruit a convenience sample of 100 units (a unit can be a dyad, a patient alone or a caregiver alone). Study participants are French-speaking, adults, preoperative cancer patients and/or their adult caregivers. The implementation and effectiveness are assessed through indicators of the RE-AIM framework: Reach, Effectiveness, Adoption, Implementation and Maintenance. Functional fitness and health outcomes are assessed pre-post intervention and 90-day post-surgery. Interviews with patients, caregivers and health professionals will be conducted to document implementation barriers, facilitators and strategies to facilitate scaling-up of the intervention across various health organisations using the Consolidated Framework for Implementation Research (CFIR). Discussion and dissemination: This study will provide evidence from various real-world cancer care settings about the implementation and effectiveness of an innovative tele-prehabilitation intervention that aims to rapidly engage cancer patients and caregivers. This intervention has the potential to accelerate and facilitate behaviour change early in the cancer continuum with the objective of optimizing the whole cancer experience and future scaling-up across a variety of cancer care units. Our team will disseminate coACTIF results through reports to stakeholders, scientific manuscripts and presentation at clinical and scientific conferences.

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)0.059
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.059
Threshold uncertainty score0.311

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.036
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0030.003
Science and technology studies0.0050.003
Scholarly communication0.0030.003
Open science0.0040.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0310.004

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 head0.370
Teacher spread0.362 · 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 designNot applicable
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

Citations1
Published2025
Admission routes2
Has abstractyes

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