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Record W2793861623

Examining the Feasibility and Effects of a Pilot Online Physical Activity Intervention Targeting Social Cognitive Variables in Youth with Physical Disabilities

2017· article· en· W2793861623 on OpenAlexaff
Ritu Sharma, Amy E. Latimer‐Cheung, John Cairney, Kelly P. Arbour‐Nicitopoulos

Bibliographic record

VenueTSpace · 2017
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsQueen's UniversityUniversity of Toronto
Fundersnot available
KeywordsSocial cognitive theoryPsychological interventionIntervention (counseling)FidelityPsychologyCognitionThe InternetPopulationRepeated measures designBehavior change methodsApplied psychologyClinical psychologyGerontologyDevelopmental psychologyMedicineEnvironmental healthPsychiatryComputer science
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate the feasibility, and short- and longer-term effects of a four-week social cognitive theory-based online physical activity (PA) intervention on the social cognitions and PA behaviour of youth with physical disabilities. Intervention feasibility was supported by high implementation fidelity (100%), high compliance (95.32%), and positive ratings on indicators of acceptability. A series of one-way, repeated measures ANOVAs revealed significant: (a) small-to-medium-sized increases in participants’ task (F[2, 11] = 5.89, η2p = .28) and barrier (F[2, 11] = 4.66, η2p = .24) self-efficacy; (b) large-sized increases in the use of goal-setting (F[2, 11] = 11.01, η2p = .42), and scheduling and planning (F[2, 11] = 10.66, η2p = .42); and (c) a medium-sized increase in PA behaviour (F[2, 11] = 5.32, η2p = .26). Key study implications and contributions to a growing field of research on technology-based PA interventions for youth are discussed.

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)0.005
metaresearch head score (Gemma)0.013
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 score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.181
GPT teacher head0.470
Teacher spread0.289 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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