Cognitive Reappraisal Moderates the Link Between Burden and Loneliness in Dementia Family Caregivers
Bibliographic record
Abstract
Abstract Caregiving for a loved one with a neurodegenerative disease is an important part of family life. However, the associated burden of experiencing declining functionality in a parent, spouse/partner, or other family member can lead to increased loneliness in caregivers. Cognitive reappraisal is the process of reevaluating one’s thoughts about a situation to alter their emotional experience (e.g., finding the good in a bad situation). Using cognitive reappraisal may be a protective strategy to reduce loneliness in caregivers in the context of increased burden. We studied a total of 345 caregivers (age: M = 64.44, SD = 11.46) caring for a family member with a neurodegenerative disease (parent = 90; spouse/partner = 255) who were participating in our research on dementia caregivers. Caregivers’ burden, loneliness, and likelihood of using cognitive reappraisal were assessed using well-established questionnaires. We found that greater caregiver burden was associated with greater loneliness (r = 0.436, p < 0.001). This association was moderated by caregivers’ use of cognitive reappraisal (B = -0.08, p = 0.02) such that the association between burden and loneliness was less pronounced for caregivers who were more likely to use cognitive reappraisal. This moderation was maintained when controlling for potentially confounding variables (age, gender, and education). We believe that cognitive reappraisal may buffer against loneliness by helping caregivers focus on the more positive aspects of the situation (e.g., areas of preserved functioning in the person with dementia or memories of pleasurable activities in the past).
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".