“Because you know there's an end to it”: Caring for a relative or friend with advanced breast cancer
Bibliographic record
Abstract
OBJECTIVE: The aim of this paper is to delineate the particular nature of the end-of-life care experience from the perspective of family members and friends. Ideas drawn from feminist analyses of caring and the sociology of death and dying are used to illuminate study respondents' accounts. METHODS: Qualitative study with people who cared for a family member or friend who died of breast cancer. Two interviews were conducted with each of 12 study participants, and one carer responded by letter to a series of questions drawn from the interview guide. Accounts were analyzed with reference to grounded theory methods. RESULTS: The analysis presented here suggests that anticipating the death of an ill relative or friend generates imperatives to care, imperatives that are both "felt" and linked to wider social structures and processes. The value assigned to closeness with the ill person can be seen to reflect cultural injunctions toward a "good death," and to shape informal carers' perceptions of their labor. Also discussed in this paper are the complex negotiations undertaken by relatives and friends, for the life and death of someone with advanced cancer. SIGNIFICANCE OF RESULTS: This paper offers insight into the social processes of caregiving, revealing how care may be shaped in particular ways by an awareness of approaching death.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".