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Development and Evaluation of an Activity Measure for People with Spinal Cord Injury

2005· article· en· W2013494612 on OpenAlexaff
Kathleen A. Martin Ginis, Amy E. Latimer‐Cheung, Audrey L. Hicks, B. Catharine Craven

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2005
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of TorontoMcMaster University
Fundersnot available
KeywordsSpinal cord injuryConvergent validityReliability (semiconductor)Content validityIntraclass correlationPsychologyPhysical activityPhysical therapyValidityPsychometricsClinical psychologyMedicineSpinal cordPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE: To develop and conduct a preliminary assessment of the content validity, test-retest reliability, and convergent validity of the Physical Activity Recall Assessment for People with Spinal Cord Injury (PARA-SCI), a new physical activity measure for people with SCI. METHODS: The scale format, interview guidelines, and activity intensity classification system were developed and content validated using qualitative and quantitative methodologies in multiple samples of people with SCI and their caregivers. Test-retest reliability (1-wk interval) was examined by administering the PARA-SCI via telephone to 102 men and women with SCI. Convergent validity was examined by assessing relationships between PARA-SCI scores and activity levels as determined by indirect calorimetry (N = 14). RESULTS: In the reliability study, intraclass correlations ranged from 0.45 to 0.91 for the various PARA-SCI activity categories and intensities. In the validity study, correlations between PARA-SCI scores and indirect calorimetry estimates of activity ranged from 0.27 to 0.88. CONCLUSIONS: The PARA-SCI shows promise as a measure of physical activity for people with SCI. Further validation research is encouraged using broader samples and alternative validation techniques.

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)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.074
GPT teacher head0.414
Teacher spread0.340 · 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

Citations153
Published2005
Admission routes1
Has abstractyes

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