Living While Dying/Dying While Living
Bibliographic record
Abstract
Palliative care involves dynamic relationships among clients, their families, and professionals, all with unique perceptions and approaches to the sociocultural construction of end-of-life care. In the home care context, this subculture may be particularly complex, because clients relate more readily as people than as patients, and professionals are not always prepared for this reality. This article presents ethnographic investigation of the culture of home-based palliative care as experienced by people older than 65 years who are dying of cancer. Through field visits to four client participants over 6 to 10 months, researchers conducted 16 interviews 1 to 2 hours long and participatory observation. Findings portray seniors' dynamic, constantly changing journey of "living while dying/dying while living." At one and the same time, seniors seized the opportunities and interpersonal relationships of "living while dying" and confronted the challenge of "dying while living" through the following: "celebrating life/grieving losses," "connecting with/detaching from others," "resigning to/accepting life circumstances," and "holding on to/moving beyond life in the present moment." The insights gained may inform nurses' provision of psychosocial end-of-life care but suggest that more education, time, and informed collegial and employer support would help to optimize their potential for this challenging role.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| 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".