Burden and Well-Being Among Caregivers: Examining the Distinction
Bibliographic record
Abstract
PURPOSE: This study assessed overall quality of life of caregivers, using a path model in which burden was conceptualized as distinct from well-being. DESIGN AND METHODS: Data were drawn from a representative sample of caregivers to dementia and nondementia care receivers in British Columbia, Canada (N = 243). The model used was based on a previously proposed stress/appraisal path model and used multiple regression path estimates. Primary stressors were care receiver cognitive status, physical function, and behavioral problems; the primary appraisal variable was hours of caregiving during the previous week. Mediators were perceived social support, frequency of getting a break, and hours of formal service use; secondary appraisal was subjective burden. The outcome measure was generalized well-being. RESULTS: Well-being was directly affected by four variables: perceived social support, burden, self-esteem, and hours of informal care. Burden was affected directly by behavioral problems, frequency of getting a break, self-esteem, and informal hours of care and was not affected by perceived social support. DISCUSSION: The finding that perceived social support is strongly related to well-being but unrelated to burden reinforces the conceptual distinctiveness of the latter two concepts. This suggests that quality of life of caregivers could be improved even with burden in their lives and that the overwhelming focus in caregiving research on burden should be supplemented with an emphasis on quality of life.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".