MétaCan
Menu
Back to cohort
Record W2000866706 · doi:10.1007/s12160-009-9149-9

Examining the Individual and Perceived Neighborhood Associations of Leisure-Time Physical Activity in Persons with Spinal Cord Injury

2010· article· en· W2000866706 on OpenAlexafffund
Kelly P. Arbour‐Nicitopoulos, Kathleen A. Martin Ginis, Philip Wilson

Bibliographic record

VenueAnnals of Behavioral Medicine · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsBrock UniversityMcMaster UniversityUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsTheory of planned behaviorStructural equation modelingPsychologyContext (archaeology)Health psychologyVariance (accounting)Explained variationSpinal cord injuryPopulationMedicinePublic healthEnvironmental healthStatisticsMathematicsControl (management)

Abstract

fetched live from OpenAlex

BACKGROUND: Theory of Planned Behavior (TPB) constructs have been shown to be useful for explaining leisure-time physical activity (LTPA) in persons with spinal cord injury (SCI). However, other factors not captured by the TPB may also be important predictors of LTPA for this population. PURPOSE: The purpose of this study is to examine the role of neighborhood perceptions within the context of the TPB for understanding LTPA in persons living with SCI. METHODS: This is a cross-sectional analysis (n = 574) using structural equation modeling involving measures of the TPB constructs, perceived neighborhood esthetics and sidewalks, and LTPA. RESULTS: TPB constructs explained 57% of the variance in intentions and 12% of the variance in behavior. Inclusion of the neighborhood variables to the model resulted in an additional 1% of the variance explained in intentions, with esthetics exhibiting significant positive relationships with the TPB variables. CONCLUSION: Integrating perceived neighborhood esthetics into the TPB framework provides additional understanding of LTPA intentions in persons living with SCI.

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 score0.038
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.165
GPT teacher head0.422
Teacher spread0.257 · 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

Citations23
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueAnnals of Behavioral MedicineSame topicUrban Transport and AccessibilityFrench-language works237,207