The buffering effect of resilience on depression among individuals with spinal cord injury: A structural equation model.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it