Family caregiver burden: results of a longitudinal study of breast cancer patients and their principal caregivers
Bibliographic record
Abstract
BACKGROUND: The vital role played by family caregivers in supporting dying cancer patients is well recognized, but the burden and economic impact on caregivers is poorly understood. We prospectively examined the psychosocial, occupational and economic impact of caring for a person with a terminal illness. METHODS: We studied 89 caregivers of women with advanced breast cancer receiving care at either the Ottawa or Hamilton regional cancer centres in Ontario. Patients were followed until their death or study completion at 3 years. Patients identified a principal caregiver to participate in the study. The Karnofsky Performance Status (KPS) index, the Medical Outcomes Study 36-item Short Form (SF-36), the Hospital Anxiety and Depression Scale, the Zarit Burden Inventory, FAMCARE and the Medical Outcomes Study Social Support Survey were administered during follow-up. Economic data were collected by means of a questionnaire administered by an interviewer. Assessments were conducted every 3 months during the palliative period (KPS score > 50) and every 2 weeks during the terminal period (KPS score < or = 50). RESULTS: Over half of the caregivers were male (55%) and the patient's spouse or partner (52%), with a mean age of 53 years. At the start of the palliative period, the caregivers' mean physical functioning score was better than the patients' (51.3 v. 35.1, 95% confidence interval [CI] 13.3-20.0); there were similar mean mental functioning scores (46.6 and 47.1 respectively); similar proportions were depressed (11% and 12%); and significantly more caregivers than patients were anxious (35% v. 19%, p = 0.009). More caregivers were depressed (30% v. 9%, p = 0.02) and had a higher level of perceived burden (26.2 v. 19.4, p = 0.02) at the start of the terminal period than at the start of the palliative period. Burden was the most important predictor of both anxiety and depression. Of employed caregivers, 69% reported some form of adverse impact on work. In the terminal period 77% reported missing work because of caregiving responsibilities. Prescription drugs were the most important component of financial burden. INTERPRETATION: Caregivers' depression and perceived burden increase as patients' functional status declines. Strategies are needed to help reduce the psychosocial, occupational and economic burden associated with caregiving.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".