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Record W2783319370 · doi:10.2196/resprot.8302

Research- and Practice-Based Nutrition Education and Cooking Workshops in Pediatric Oncology: Protocol for Implementation and Development of Curriculum

2018· article· en· W2783319370 on OpenAlexafffundvenue
Cynthia Chaput, Sabrina Beaulieu-Gagnon, Véronique Bélanger, Simon Drouin, Laurence Bertout, Lucie Lafrance, Cinthia Olivier, Marthe Robitaille, Caroline Laverdière, Daniel Sinnett, Marie Marquis, Valérie Marcil

Bibliographic record

VenueJMIR Research Protocols · 2018
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversité de MontréalUniversité LavalCentre Hospitalier Universitaire Sainte-Justine
FundersFondation Charles-BruneauUniversité de Montréal
KeywordsMedicineChildhood cancerCancerCurriculumPediatric oncologyProtocol (science)Pediatric cancerFamily medicineAlternative medicineInternal medicinePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Progresses in childhood cancer treatment, diagnosis, and management have resulted in childhood cancer survival rates of over 80%. However, this therapeutic success comes with a heavy price: two-thirds of childhood cancer survivors will be affected by further complications, including cardiovascular and metabolic diseases. Adequate nutrition during cancer treatment is essential to ensure the child's optimal development, improve tolerance to treatments, and can contribute to lower the risk of developing cardiometabolic diseases. Side effects of cancer treatments can negatively impact children's nutritional intake and eating behaviors. Involving the families of childhood cancer patients in educational workshops could be a promising avenue to promote healthy eating during and after cancer treatment. OBJECTIVE: The objectives of this study were to develop, validate, and implement a family-based nutrition education and cooking workshop curriculum in a pediatric oncology setting that addresses the nutritional issues encountered during treatments while promoting the adoption of healthy eating habits for the prevention of long-term cardiometabolic effects. METHODS: The workshops were developed and validated following an 8-step iterative process, including a review of the literature and consultations with a steering committee. An evaluation tool was also developed. A nonrandomized study protocol was elaborated to implement the workshops and measure their impact. The themes of the 6 research- and practice-based lessons are as follows: meal fortification during cancer treatment, changes in taste during cancer therapy and their impact on children, adapting diet to eating-related side effects of treatments, nutritional support during cancer treatment, Mediterranean diet and health, and planning quick and economic meals. The validation process included consultations with the institution's clinical nutrition professionals. Self-administered post questionnaires were developed according to the content of each workshop to measure short-term outcomes, namely, participants' perception of knowledge acquisition, behavioral intention, and satisfaction. Medium-term outcomes that will be evaluated are participants' anthropometric profile, quality of the diet, and circulating biomarkers of metabolic health. RESULTS: The project was funded in 2016 and enrollment will be completed in 2021. Data analysis is currently under way and the first results are expected to be submitted for publication in 2019. CONCLUSIONS: This research- and practice-based nutrition education and cooking demonstration curriculum could be a valuable complement to a multidisciplinary lifestyle intervention for the prevention of long-term cardiometabolic complications in childhood cancer.

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)0.076
metaresearch head score (Gemma)0.062
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 score0.076
Threshold uncertainty score0.403

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0760.062
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0050.004
Science and technology studies0.0070.003
Scholarly communication0.0040.004
Open science0.0060.006
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0430.013

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 head0.346
GPT teacher head0.655
Teacher spread0.310 · 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".

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Citations14
Published2018
Admission routes3
Has abstractyes

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