The buffering effect of resilience on depression among individuals with spinal cord injury: A structural equation model.
Bibliographic record
Abstract
OBJECTIVE: To translate the theoretical constructs from a model of resilience into a structural equation model and evaluate relationships among the model's theoretical constructs associated with resilience and the occurrence of depressive symptoms. DESIGN: Quantitative descriptive research design using structural equation modeling (SEM). PARTICIPANTS: Two-hundred and fifty-five individuals with SCI recruited from the Canadian Paraplegic Association (CPA). OUTCOME MEASURES: Outcome was measured by the Center for Epidemiologic Studies-Depression Scale. RESULTS: The resilience model fit the data relatively well: χ² (200, N = 255) = 451.57, p < .001; χ²/df = 2.26; CFI = .92, RMSEA = 0.070 (90% CI: 0.062-0.079), explaining 77% of the variance in depressive symptomatology. Severity of SCI-related stressors significantly influenced perceived stress (β = .60) and perceived stress, in turn, affected depressive symptoms (β = .66), characteristics of resilience (β = -.43), and social support (β = -.26). The resilience characteristics had an inverse relationship with depressive symptoms (β = -.29). No direct relationship was found between severity of SCI-related stressors and depressive symptoms. CONCLUSIONS: Findings provide support for the resilience model and suggests characteristics of resilience "buffer" the perceptions of stress on depressive symptoms. The resilience model may be useful to guide clinical interventions designed to improve the mental health of individuals with SCI.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".