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How haematological cancer nurses experience the threat of patients’ mortality

2011· article· en· W2140156005 on OpenAlexaffabout
Doris Leung, Mary Jane Esplen, Elizabeth Peter, Doris Howell, Gary Rodin, Margaret I. Fitch

Bibliographic record

VenueJournal of Advanced Nursing · 2011
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsSunnybrook Health Science CentrePrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineFeelingDistressDiseaseNursingFamily medicinePsychologySocial psychologyClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.231
GPT teacher head0.464
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2011
Admission routes2
Has abstractyes

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