Association of depression and pain interference with disease-management self-efficacy in community-dwelling individuals with spinal cord injury
Bibliographic record
Abstract
OBJECTIVE: To determine factors influencing disease-management self-efficacy in individuals with spinal cord injury. DESIGN: A cross-sectional study. SUBJECTS/PATIENTS: Forty-nine community-dwelling individuals with chronic spinal cord injury (mean age 44 years) participated in the study. METHODS: Each subject was evaluated for disease-management self-efficacy (Self-efficacy for Managing Chronic Disease), depression (10-item Center for Epidemiologic Studies Depression Scale), pain interference (Pain Interference Scale), and availability of support (Interpersonal Support Evaluation List short form). Multiple regression analysis was performed to determine the relative contributions of these factors to disease-management self-efficacy. RESULTS: The mean disease-management self-efficacy score was 6.5 out of 10 (standard deviation 1.6). Bivariate correlation analysis showed that higher self-efficacy was significantly correlated with longer time since injury (r = 0.367, p = 0.010), better social support (r = 0.434, p = 0.002), lower pain interference (r = -0.589, p <0.001), and less severe depressive symptoms (r=-0.463, p=0.001). In multiple regression analysis, only lower pain interference and less severe depressive symptoms were significantly associated with higher disease-management self-efficacy (F 4,44=10.249, R2=0.482, p<0.001). CONCLUSION: Disease-management self-efficacy is suboptimal in many community-living people with spinal cord injury. This research suggests that rehabilitation of patients with spinal cord injury should include self-efficacy-enhancing strategies. Alleviation of depressive symptoms and pain self-management may be important for improving disease-management self-efficacy in this population, but this requires further study.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".