How haematological cancer nurses experience the threat of patients’ mortality
Bibliographic record
Abstract
AIM: This article explores how cancer nurses experienced the threat of patients' mortality on malignant haematology units of one institution in Ontario, Canada. BACKGROUND: Although patients with cancer are living longer with bone marrow transplantations, still they face possibilities of dying due to complications from treatment and their disease. METHODS: Interpretive phenomenology guided the process. Nineteen front-line registered nurses were purposively recruited from two inpatient bone marrow transplant units. Focused observations and individual interviews were analysed. Data were collected from April to August 2007. RESULTS: The major findings emphasized nurses' internal conflict related to their simultaneous need to help patients fight their disease and to prepare them for the possibility of letting go. The authors used the terms 'letting go', not to reflect nurses' intents to abandon life but to release patients from perceived norms of the 'curative culture'. Nurses experienced 'bursting the bubble of hope' by circumstances not in their control, and were often not certain whether or not to respond and how to respond to the distress of patients and families about death and dying. When feeling reassured of meeting patients' and families' expectations, nurses enabled patients and families to let go when further treatment was futile, prevented technological intrusions, and helped patients have 'easier' deaths. CONCLUSION: Results suggest enhancing nurses' capacity to negotiate more effectively the contradictory clinical tasks of fighting disease and preparing patients for the end of life. In this regard, nurses may minimize patients' distress by providing opportunities for them to share their fears and have them validated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".