MétaCan
Menu
Back to cohort
Record W2062736206 · doi:10.1080/09638280410001714772

Psychometric validation of a subjective well-being measure for people with spinal cord injuries

2004· article· en· W2062736206 on OpenAlexaboutno aff
Martha H. Chapin, Susan M. Miller, James M. Ferrin, Fong Chan, Stanford E. Rubin

Bibliographic record

VenueDisability and Rehabilitation · 2004
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsnot available
FundersNational Institute on Disability and Rehabilitation Research
KeywordsMeasure (data warehouse)Spinal cord injuryPsychologyPhysical medicine and rehabilitationRehabilitationMedicineSpinal cordPsychometricsClinical psychologyPhysical therapyPsychiatryComputer scienceData mining

Abstract

fetched live from OpenAlex

PURPOSE: The researchers examined the factorial validity and the concurrent validity of the Sense of Well-Being Inventory (SWBI) based on a sample of Canadians with spinal cord injuries (SCI) in the community. METHOD: One hundred thirty-two participants were recruited from the Alberta, Saskatchewan, Nova Scotia, and Manitoba chapters of the Canadian Paraplegic Association. Mean age of participants was 45.82 years (SD=15.67), and 77% were men. The participants were asked to complete a research packet containing a demographic questionnaire, the SWBI, and the brief version of the World Health Organization Quality of Life questionnaire (WHOQOL-BREF). RESULTS: Factor analysis yield four factors (Psychological Well-Being, Financial Well-Being, Social and Family Well-Being, and Physical Well-Being) similar to the original SWBI. In addition, the SWBI factors in the present study correlated moderately well with the corresponding factors in the WHOQOL-BREF and with demographic variables appropriate to the respective subscale. CONCLUSIONS: The factorial validity and the concurrent validity of the SWBI were generally supported. The SWBI, as a subjective well-being measure developed specifically to relate to disability and rehabilitation, appears useful for use with people with SCI in the community.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.024
GPT teacher head0.360
Teacher spread0.335 · 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

Citations43
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueDisability and RehabilitationSame topicSpinal Cord Injury ResearchFrench-language works237,207