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Record W4328124768 · doi:10.1080/1091367x.2023.2192194

Adaptation to Spanish and Validity by Wearable Sensors of the Physical Activity Recall Assessment for People with Spinal Cord Injury

2023· article· en· W4328124768 on OpenAlexaff
Alex Castan, Eloy Opisso, Andrés Chamarro Lusar, Kathleen A. Martin Ginis, Joan Saurí

Bibliographic record

VenueMeasurement in Physical Education and Exercise Science · 2023
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsIntraclass correlationSpinal cord injuryPsychologyReliability (semiconductor)Physical medicine and rehabilitationWearable computerPhysical therapyPhysical activityRecallPsychometricsDevelopmental psychologyMedicineSpinal cordComputer scienceCognitive psychologyPsychiatry

Abstract

fetched live from OpenAlex

No Spanish-language tool exists to assess physical activity in people with spinal cord injuries. This work aimed to provide a valid Spanish version of the Physical Activity Recall Assessment for People with Spinal Cord Injury (PARA-SCI). It was conducted in three phases. First, translation and cross-cultural adaptation. Second, a reliability assessment through inter-rater reliability (n = 25) and test – retest reliability (n = 50). Third, validity assessment, comparing PARA-SCI-Spanish and wearable sensors (n = 13), as well as analyzing PARA-SCI-Spanish results between known groups (n = 235). Intraclass correlation coefficients ranged from 0.36 to 0.95. The correlations between the wearable sensors and the PARA-SCI-Spanish were r = 0.67, p = .005 (moderate to vigorous physical activity) and r = 0.62, p = .01 (total activity). The known-groups analyses demonstrated differences in accord with previous research and not differing according to the interview mode. The PARA-SCI-Spanish was a reliable and valid tool in telephone and face-to-face interviews.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.104
GPT teacher head0.422
Teacher spread0.317 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueMeasurement in Physical Education and Exercise ScienceSame topicSpinal Cord Injury ResearchFrench-language works237,207