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Record W4213024481 · doi:10.1177/14604582221075560

Understanding rural-living young adult cancer survivors’ motivation during a telehealth behavior change intervention within a single-arm feasibility trial

2022· article· en· W4213024481 on OpenAlexaff
Jenson Price, Jennifer Brunet

Bibliographic record

VenueHealth Informatics Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsOttawa HospitalMontfort HospitalUniversity of Ottawa
Fundersnot available
KeywordsTelehealthIntervention (counseling)Context (archaeology)Behavior changePsychologyGerontologyClinical psychologyMedicineTelemedicineHealth carePsychiatrySocial psychology

Abstract

fetched live from OpenAlex

A single-arm feasibility trial was conducted to explore rural-living young adult cancer survivors’ physical activity, fruit and vegetable consumption, and motivational processes underlying any behavior changes during a telehealth behavior change intervention grounded in self-determination theory. Participants ( n = 7; 85.7% female; Mage = 33.9, range = 28–37) met with a health coach once a week for 60 min for 12 weeks. Participants completed pre- and post-intervention surveys that assessed their behaviors, basic psychological needs satisfaction, and behavioral regulations. Participants also completed a semi-structured interview post-intervention. Quantitative results indicate behavioral outcomes, basic psychological needs satisfaction, and behavioral regulations increased from pre- to post-intervention. Five themes provide context for the observed increases. Results provide preliminary evidence that motivation for physical activity and fruit and vegetable consumption may be facilitated by a one-on-one telehealth intervention among rural-living young adult cancer survivors. Large scale studies are needed to determine effectiveness of the intervention and identify mechanisms underpinning behavioral outcomes.

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)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.251
GPT teacher head0.392
Teacher spread0.141 · 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 designNon-randomized 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

Citations20
Published2022
Admission routes1
Has abstractyes

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