Caregiving for women with advanced breast cancer
Bibliographic record
Abstract
AIM: To describe the psychosocial impact on caregivers of caring for women with advanced breast cancer. METHODS: Five focus groups were held with bereaved caregivers. Qualitative content analysis of the transcripts was conducted to identify emerging themes. RESULTS: Two categories of caregivers were identified: spouse caregivers (SCGs) and non-spouse caregivers (NSCGs), who were either close friends or relatives. SCGs and the patient managed care cooperatively and shared care-related decision making. Working SCGs managed multiple roles but employers gave them support and freedom to take the time that was necessary to care for their wives. NSCGs had the most life roles to manage, and saw themselves as agents for the patient. The terminal phase of disease was most burdensome for all caregivers, although NSCGs had the most difficulties. During this phase, the patients' activities of daily living became much more impaired. In addition, some patients were not willing to receive continence support from caregivers, and some caregivers found that they could not provide continence support. SCGs were able to negotiate these care-related roles with their spouse, but NSCGs struggled to satisfy the wishes of the patient. CONCLUSION: Caregivers assume great responsibility for providing care, particularly during the terminal phase. Caregiving becomes more complex with each additional life role of the caregiver. SCGs have two advantages: (1) living with the patient facilitates caregiving and (2) patterns of decision making that were established previous to the illness facilitated shared decision making between the patient and spouse caregiver.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".