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Record W4411426612 · doi:10.1177/20551029251349874

Explaining adherence to contrasted physical activity and nutrition scenarios in post-treatment childhood cancer patients: A cross-sectional study using variables from the Theory of Planned Behavior

2025· article· en· W4411426612 on OpenAlexafffund
Ariane Lévesque, Daniel Curnier, Valérie Marcil, Maxime Caru, Caroline Laverdière, Émélie Rondeau, Caroline Meloche, Véronique Bélanger, Isabelle Bouchard, Daniel Sinnett, Serge Sultan

Bibliographic record

VenueHealth Psychology Open · 2025
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersCanadian Institutes of Health ResearchCHU Sainte-Justine FoundationFondation Charles-Bruneau
KeywordsTheory of planned behaviorCross-sectional studyPhysical activityPsychologyClinical psychologyChildhood cancerCancerMedicineGerontologyPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Children diagnosed with cancer are vulnerable to long-term health issues. Engaging in physical activity (PA) and adopting a healthy diet could mitigate these risks. This study aimed to understand the role of variables from the Theory of Planned Behavior (TPB) in adherence to healthy/unhealthy PA and nutrition scenarios. Through convenience sampling, four ad hoc questionnaires measuring variables from the TPB were completed by 96 parents of children diagnosed with cancer in paper format or via a secure online platform to assess attitude, perceived behavioral control (PBC), subjective norms (SN), and intention. We performed a MANOVA and multiple linear regressions. We found an effect of behavior domain (F(3, 4828.66) = 6.467, p < 0.001, ηp2 = 0.004), and scenario (F(3, 152.86) = 76.495, p < 0.001, ηp2 = 0.600). Intention was a complete intermediary variable between attitude/SN and healthy nutrition. Attitude, PBC, and intention are promising targets for PA and nutrition behaviors. SN should also be targeted for nutrition behaviors.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.128
GPT teacher head0.509
Teacher spread0.381 · 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 designObservational
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

Citations0
Published2025
Admission routes2
Has abstractyes

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