The Relation of Pain and Caregiver Burden in Informal Older Adult Caregivers
Bibliographic record
Abstract
OBJECTIVE: Pain in older adults is highly prevalent and although informal caregiving is commonly provided by an older cohort, the relationship between pain and caregiving has seldom been examined. Our goal was to study the associations between caregiver pain, depression, and caregiver burden in a sample of older adult caregivers. DESIGN: Questionnaires were completed by 116 caregivers (mean age=73.34) to measure the caregivers' overall pain, chronic pain status, caregiver burden and its five dimensions, depression, and the care recipients' level of disability. Hierarchical linear regression analyses evaluated the extent to which care recipient and caregiver variables, including caregiver pain and depression, were related to high levels of caregiver burden. RESULTS: The overall level of pain reported by the caregiver was a significant predictor of overall caregiver burden and the emotional and physical dimensions of caregiver burden, whereas a number of care recipient variables (e.g., disability level) were significant predictors of the social, emotional, and time dependence dimensions of caregiver burden. CONCLUSIONS: This is the first study to investigate the relationships among caregiver pain and caregiver burden in informal older adult caregivers. We conclude that the role of caregiver pain has been greatly underestimated in the caregiver burden literature and suggest a need for interdisciplinary collaboration for effective management of caregiver burden in older adults.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".