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Record W2118680690 · doi:10.1310/pma9-yr1e-twjx-v90w

Social Support and Aging with a Spinal Cord Injury: Canadian and British Experiences

2001· article· en· W2118680690 on OpenAlexaffabout
Mary Ann McColl, Robert W. Arnold, Susan Charlifue, Ken Gerhart

Bibliographic record

VenueTopics in Spinal Cord Injury Rehabilitation · 2001
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsSocial supportMedicineAffect (linguistics)DemographicsLife satisfactionSpinal cord injuryEmotional supportLongitudinal studyGerontologyDemographyClinical psychologySpinal cordPsychologyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

The study set out to describe the support systems of a large multicenter sample of people with long-standing spinal cord injuries (SCIs) and to explore the effects of age, duration of injury, problems, demographics, and well-being on reporting of social support. It was part of a longitudinal study of aging and SCI, involving British and Canadian participants. A total of 290 participants were recruited from four large, well-established databases in the United Kingdom and Canada. The sample included individuals with an average age of 57 years and an average duration of 33 years. The study showed that informational support was perceived by participants as less available to them than either instrumental or emotional support. Further, it showed that age has a direct negative effect on satisfaction with social support but an indirect positive effect on availability of support, mediated by well-being. Disability, on the other hand, has an indirect negative effect on satisfaction, mediated by problems. According to the model developed and tested here, the availability of social support has a significant negative direct effect on the experience of problems, but the presence or absence of problems does not affect the availability of social support.

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 score0.585
Threshold uncertainty score1.000

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 studies0.0000.001
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 head0.039
GPT teacher head0.396
Teacher spread0.357 · 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

Citations13
Published2001
Admission routes2
Has abstractyes

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