An Integrative Exploration of Psychological Resilience in Informal Caregivers of Persons with Multiple Sclerosis
Bibliographic record
Abstract
Informal caregiving is growing in demand and is far from a uniform experience. Some caregivers report burdensome effects, while others attest to a wide range of benefits associated with their role. In the context of informal caregivers of persons affected by chronic neurological conditions (CNCs), psychological resilience is increasingly being explored as a protective factor that may account for variability in the caregiver experience; however, multiple sclerosis (MS) caregivers are noticeably absent from this body of work. To synthesize current evidence concerning resilience conceptualizations, assessments, and health correlates within this population, this thesis included a systematic review of resilience in CNC informal caregivers in which MS caregivers were unrepresented. Following this review, a qualitative study was conducted in informal MS caregivers to ascertain MS caregivers’ conceptualizations and unique lived experiences of resilience. Twenty-four semi-structured interviews of Canadian MS informal caregivers were conducted. Informed by the socioecological model of resilience in caring relationships, transcripts were analyzed using flexible thematic analysis. In support of the conceptual ambiguity of resilience, caregivers did not concur on a single resilience conceptualization. Emergent themes contributed to the creation of a cyclical model of resilience that incorporates adversity in the form of continuous loss and obstructed health-related self-care, individual and community resources, and multilevel adaptive pathways. We use our model to prompt future research directions and inform the development of effective resilience-enhancing interventions for MS caregivers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".