Impact of psychological characteristics in self-management in individuals with traumatic spinal cord injury
Bibliographic record
Abstract
STUDY DESIGN: Cross-sectional survey. OBJECTIVE: To examine the association between psychological characteristics in self-management and probable depression status in individuals with a traumatic spinal cord injury (SCI). SETTING: Community-dwelling individuals with traumatic SCI living across Canada. METHODS: Individuals with SCI were recruited by email via the Rick Hansen Institute as well as an outpatient hospital spinal clinic. Data were collected by self-report using an online survey. Standardized questionnaires were embedded within a larger survey and included the Hospital Anxiety and Depression Scale (HADS), the short version of the Patient Activation Measure (PAM), the Moorong Self-Efficacy Scale (MSES) and the Pearlin-Schooler Mastery Scale (PMS). RESULTS: Individuals with probable depression (n=25) had lower self-efficacy (67.9 vs 94.2, P<0.0001), mastery (18.9 vs 22.9, P<0.0001) and patient activation (60.4 vs 71.6, P<0.0001) as well as higher anxiety (9.0 vs 5.5, P<0.0001), compared with their non-depressed counterparts (n=75). A logistic regression determined that lower self-efficacy and mastery scores as well as less time since injury were associated with depression status (P=0.002; P=0.02 and P=0.02, respectively). Individuals with higher anxiety scores were almost 1.5 times more likely to be depressed, while older age was positively associated with depression status (P=0.016 and P=0.024, respectively). CONCLUSION: Interventions for depression in SCI, including a self-management program, should target factors such as self-efficacy and mastery, which could improve secondary medical complications and overall quality of life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".