Lifestyle interference and emotional distress in family caregivers of advanced cancer patients
Bibliographic record
Abstract
BACKGROUND: Providing end-of-life care at home to a family member with advanced cancer can have a negative impact on the emotional well-being of the family caregiver. The current study examined the impact of providing care on lifestyle and emotional well-being in a sample of caregivers to patients with advanced cancer. The mediation of lifestyle interference between the amount of care provided and emotional distress was specifically examined. METHODS: Forty-four family caregivers participated in a structured quantitative interview. Lifestyle interference was assessed by the Caregiving Impact Scale, amount of care provided was assessed by the Caregiver Assistance Scale, and emotional distress was assessed by the Profile of Mood States-Short Form. Pearson and partial correlations tested whether lifestyle interference mediated the relationship between caregiving assistance and emotional distress. Regression analyses determined overall correlates of emotional distress. RESULTS: Three criteria, required to substantiate mediation, were met for total mood disturbance and the depression and tension subscales. An overall regression model identified education level and lifestyle interference to be significant and unique correlates of emotional distress. CONCLUSIONS: The current results suggest that caregivers experience increased emotional distress, regardless of the amount of care provided, when limited in their ability to participate in valued activities and interests. In addition, caregivers with less than a high school education experience more emotional distress. Therefore, helping caregivers maintain valued aspects of their lifestyle should be an important element of home care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".