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Record W3034433076 · doi:10.1123/apaq.2019-0103

Videoconference-Delivered Group-Based Physical Activity Self-Regulatory Support for Adults With Spinal Cord Injury: A Feasibility Study

2020· article· en· W3034433076 on OpenAlexaff
Samantha Jeske, Lawrence R. Brawley, Kelly P. Arbour‐Nicitopoulos

Bibliographic record

VenueAdapted Physical Activity Quarterly · 2020
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of SaskatchewanUniversity of Toronto
Fundersnot available
KeywordsFacilitatorAttendanceVideoconferencingPhysical therapySpinal cord injuryPsychological interventionMedicinePsychologyPhysical medicine and rehabilitationClinical psychologyNursingSpinal cordMultimediaSocial psychologyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Videoconferencing is a novel method for overcoming time and transportation barriers to leisure-time physical activity (LTPA) interventions. This study examined the feasibility of a group videoconference intervention on LTPA self-regulatory skills training in a sample of nine adults with spinal cord injury (SCI). Session implementation checklists and self-report surveys were administered during four weekly sessions to assess intervention management, group processes, intervention resources, and initial efficacy. Attendance rate was high (91.7%), and the average weekly session duration was 79.6 min. Participants reported high ratings of group cohesion, facilitator collaboration, session content comprehension, and ease in operating the videoconference platform. Knowledge sharing among the group ranged from 18 to 58 exchanges per session, demonstrating learning and group cohesion. LTPA frequency increased among 44% of participants, and 22% of participants achieved the SCI-specific aerobic guidelines. Overall, group videoconferencing holds promise for LTPA support among adults with SCI. Long-term research is warranted to test LTPA self-regulatory and behavioral effects.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.358
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.343
Teacher spread0.285 · 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.

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

Citations8
Published2020
Admission routes1
Has abstractyes

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