The Experiences of Cancer Nurses’ Existential Care in Response to the Threat of Patients' Mortality within the Culture of Cure
Bibliographic record
Abstract
Patients are living longer with many types of cancer; however, often they face sudden possibilities of dying, not only due to their advancing illness but due to complications of their treatment. Consequently, they can express substantial existential distress. Nurses’ close proximity to patients puts them in an ideal place to assess and engage with patients’ existential distress; yet this kind of research has been scarce. The purpose of this doctoral thesis was to explore nurses’ experiences of being with patients facing the threat of mortality. Yalom describes this threat as the fear of death, isolation, anxiety and responsibility about freedom, and meaninglessness. The study took place in a cancer setting where care is highly technological and goals of cure dominate, specifically, two bone marrow transplant units of one institution in Canada. Benner’s methodology of interpretive phenomenology guided data collection and analysis of focused observations and interviews with 19 registered nurses. The experience of fighting cancer while preparing for the possibility of letting go was the main theme. Letting go did not reflect nurses’ intents to abandon life but to release patients (if only briefly) from perceived norms of the curative culture. More specifically, the main theme was characterized by: 1) working within the culture of cure and the possibilities of patients dying, 2) concern about “bursting the bubble of hope,” 3) whether to and how to respond to patients’ distress and dying, and 4) coping with patient involvement. In the context of responsive relationships (patients and their families, and healthcare colleagues), nurses reported engaging in communication about the threat of patients’ mortality, and responding with letting be and supporting families to let go, the management of technology and prevention of technological intrusions, and striving for patients to have “easier” deaths. Results indicate a potential to enhance nurses’ supportive care constituted by their perceived responsibility to engage and respond to patients’ existential distress. Moreover, this study suggests that more attention is warranted not only to policy, education, and research that focuses on patients’ existential well-being, but to the well-being of nurses working within tensions of curing and comforting.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| 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".