Towards conceptual convergence: A systematic review of psychological resilience in family caregivers of persons living with chronic neurological conditions
Bibliographic record
Abstract
BACKGROUND: The demand for family caregiving in persons with chronic neurological conditions (CNCs) is increasing. Psychological resilience may empower and protect caregivers in their role. Thus, a synthesis of resilience evidence within this specific population is warranted. AIM: In this systematic review we aimed to: (1) examine the origins and conceptualizations of resilience; (2) summarize current resilience measurement tools; and (3) synthesize correlates, predictors and outcomes of resilience in family caregivers of persons with CNCs. DESIGN: We sourced English articles published up to July 2020 across five databases using search terms involving CNCs, family caregivers and resilience. RESULTS: A total of 50 studies were retained. Nearly half (44%) of the studies used trait-based resilience definitions, while about one third (36%) used process-based definitions. Twelve different resilience scales were used, revealing mostly moderate to high-resilience levels. Findings confirmed that resilience is related to multiple indicators of healthy functioning (e.g., quality of life, social support, positive coping), as it buffers against negative outcomes of burden and distress. Discordance relating to the interaction between resilience and demographic, sociocultural and environmental factors was apparent. CONCLUSIONS: Incongruity remains with respect to how resilience is defined and assessed, despite consistent definitional concepts of healthy adaptation and equilibrium. The array of implications of resilience for well-being confirms the potential for resilience to be leveraged within caregiver health promotion initiatives via policy and practice. PATIENT OR PUBLIC CONTRIBUTION: The findings may inform future recommendations for researchers and practitioners to develop high-quality resilience-building interventions and programmes to better mobilize and support this vulnerable group.
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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 | 0.027 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 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".