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Record W3157359041 · doi:10.24908/iqurcp.8757

Ready, Set, Go: Increasing Awareness of the Social Benefits of Being Active Among People with Spinal Cord Injury

2016· article· en· W3157359041 on OpenAlexvenueno aff
Julie Crowson

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2016
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAffect (linguistics)Spinal cord injuryStigma (botany)ConsciousnessSet (abstract data type)Physical activitySocial psychologyMedicineSpinal cordPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Previous research has shown that presenting yourself as an “exerciser” through participation in physical activity (PA) can help alleviate stigma associated with having a disability. The purpose of this study is to explore whether brief messages emphasizing the social benefits of PA affect intentions to engage in PA among people with spinal cord injury (SCI). Ninety inactive adults with SCI will respond to a baseline questionnaire and will receive a message promoting the social benefits of PA over the telephone. In a two-week follow-up interview, affective and instrumental attitudes, intentions, and stigma consciousness will be measured. Data is currently being collected and preliminary results will be presented. It is hypothesized that messages promoting social benefits of PA will lead to more positive attitudes and increased intentions to engage in PA.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.129
GPT teacher head0.428
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2016
Admission routes1
Has abstractyes

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