Understanding Quality of Life in Adults with Spinal Cord Injury Via SCI-Related Needs and Secondary Complications
Bibliographic record
Abstract
BACKGROUND: Understanding the factors that can predict greater quality of life (QoL) is important for adults with spinal cord injury (SCI), given that they report lower levels of QoL than the general population. OBJECTIVES: To build a conceptual model linking SCI-related needs, secondary complications, and QoL in adults with SCI. Prior to testing the conceptual model, we aimed to develop and evaluate the factor structure for both SCI-related needs and secondary complications. METHODS: Individuals with a traumatic SCI (N = 1,137) responded to an online survey measuring 13 SCI-related needs, 13 secondary complications, and the Life Satisfaction Questionnaire to assess QoL. The SCI-related needs and secondary complications were conceptualized into factors, tested with a confirmatory factor analysis, and subsequently evaluated in a structural equation model to predict QoL. RESULTS: The confirmatory factor analysis supported a 2-factor model for SCI related needs, χ(2)(61, N = 1,137) = 250.40, P <.001, comparative fit index (CFI) = .93, root mean square error of approximation (RMSEA) = .05, standardized root mean square residual (SRMR) = .04, and for 11 of the 13 secondary complications, χ(2)(44, N = 1,137) = 305.67, P < .001, CFI = .91, RMSEA = .060, SRMR = .033. The final 2 secondary complications were kept as observed constructs. In the structural model, both vital and personal development unmet SCI-related needs (β = -.22 and -.20, P < .05, respectively) and the neuro-physiological systems factor (β = -.45, P < .05) were negatively related with QoL. CONCLUSIONS: Identifying unmet SCI-related needs of individuals with SCI and preventing or managing secondary complications are essential to their QoL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".