Mapping Resilience: Structural Equation Modeling of Psychological Resilience in Multiple Sclerosis Care Partners
Bibliographic record
Abstract
ABSTRACT BACKGROUND Care partners are essential supports to individuals with multiple sclerosis (MS). Both negative and positive outcomes associated with the caregiving role have been reported. Psychological resilience may be an important factor influencing the MS caregiving experience, but an MS-specific model of care partner resilience has yet to be established. This study sought to explore an explicit model of MS care partner resilience. METHODS Cross-sectional data from 471 Canadian MS care partners were collected via an online survey. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were used to test measures within a hypothesized model of resilience. Resilience was measured using the 25-item Connor-Davidson Resilience Scale. RESULTS Following CFA, the hypothesized model was simplified due to the poor fit of several variables. The final model yielded a moderate SEM fit (χ2 = 6030.95, P < .01). Being a woman was associated with greater caregiving tasks (β = 0.53, P < .001) and poorer spiritual health (β = –0.35, P < .001). Spiritual health, but not caregiving tasks, had a positive impact on both positive (β = 0.48, P < .01) and negative coping (β = 0.49, P = .01). Quality of life and resilience did not have relationships with other variables in the model. However, quality of life had a positive, unidirectional influence on resilience (β = 0.83, P < .01). CONCLUSIONS Our findings indicate that spiritual health is an important predictor of coping and should be further explored in MS care partners. Quality of life may act as a precursor to resilience within MS care partners. Further research and exploration into MS care partner resilience is warranted to confirm this exploratory model.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".