MétaCan
Menu
Back to cohort
Record W2044012029 · doi:10.1007/s12160-012-9370-9

Predictors of Leisure Time Physical Activity Among People with Spinal Cord Injury

2012· article· en· W2044012029 on OpenAlexafffund
Kathleen A. Martin Ginis, Kelly P. Arbour‐Nicitopoulos, Amy E. Latimer‐Cheung, Andrea C. Buchholz, Steven R. Bray, B. Catharine Craven, Keith C. Hayes, Mary Ann McColl, Patrick J. Potter, Karen Smith, Dalton L. Wolfe, Richard Goy, Julie Horrocks

Bibliographic record

VenueAnnals of Behavioral Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsWestern UniversityUniversity of GuelphMcMaster UniversityQueen's UniversityToronto Rehabilitation InstituteUniversity of Toronto
FundersCanadian Institutes of Health ResearchOntario Neurotrauma Foundation
KeywordsPhysical activityInternational Classification of Functioning, Disability and HealthLogistic regressionSpinal cord injuryLeisure timeHealth psychologyPsychologyLeisure activityActivities of daily livingPhysical therapyRehabilitationGerontologyMedicinePublic healthSpinal cordPsychiatrySocial psychologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Most studies of physical activity predictors in people with disability have lacked a guiding theoretical framework. Identifying theory-based predictors is important for developing activity-enhancing strategies. PURPOSE: To use the World Health Organization's International Classification of Functioning, Disability and Health (ICF) framework to identify predictors of leisure time physical activity among people with spinal cord injury (SCI). METHODS: Six hundred ninety-five persons with SCI (M age=47; 76% male) completed measures of Body Functions and Structures, Activities and Participation, Personal Factors, and Environmental Factors at baseline and 6-months. Activity was measured at 6 and 18 months. Logistic and linear regression models were computed to prospectively examine predictors of activity status and activity minutes per day. RESULTS: Models explained 19%-25% of variance in leisure time physical activity. Activities and Participation and Personal Factors were the strongest, most consistent predictors. CONCLUSIONS: The ICF framework shows promise for identifying and conceptualizing predictors of leisure time physical activity in persons with disability.

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)0.001
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 score0.062
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.123
GPT teacher head0.460
Teacher spread0.337 · 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

Citations35
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueAnnals of Behavioral MedicineSame topicSpinal Cord Injury ResearchFrench-language works237,207