Profiles of resilience in multiple sclerosis family care-partners: A Canadian cross-sectional study
Bibliographic record
Abstract
Background Psychological resilience may play an important role in protecting multiple sclerosis care-partners from the negative effects of their support role. However, predictors of resilience in this population have yet to be identified. Objectives To identify characteristics predictive of psychological resilience in multiple sclerosis care-partners as informed by the Ecological Model of Resilience. Methods Informal multiple sclerosis care-partners ( n = 540) completed an online survey. Psychological resilience was measured using the 25-item Connor-Davidson Resilience Scale. Sociodemographic and care-context predictors of resilience were analyzed using hierarchical regression. Results The mean resilience score was 59.0 ( SD = 7.6) out of a possible 100. Sociodemographic variables accounted for 31% of the variance in resilience scores in multiple sclerosis care-partners. When care-context variables were incorporated into the model, 55% of variance was explained ( F[7,320] = 26.824, p < 0.001). Each group of variables remained significant in both low disability and high disability models. Social support was the only individual variable that remained significant across all models ( p < 0.05). Conclusions Multiple sclerosis care-partners differ strikingly from other caregiving populations. Both sociodemographic and care-context variables were found to promote or hinder resilience in multiple sclerosis care-partners. Social support, in particular, may be an important target for promoting resilience in multiple sclerosis care-partners and could be leveraged in future initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".