Psychometric validation of a subjective well-being measure for people with spinal cord injuries
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".