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Palliative sedation: Nurses’ perceptions

2006· article· en· W2155419743 on OpenAlexaffabout
Alexandra Beel, Pamela Hawranik, Susan McClement, Paul Daeninck

Bibliographic record

VenueInternational Journal of Palliative Nursing · 2006
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsCancerCare ManitobaUniversity of ManitobaRiverview Hospital
Fundersnot available
KeywordsPalliative sedationPalliative careMedicineSedationPerceptionNursingPsychologyAnesthesia

Abstract

fetched live from OpenAlex

Patients with advanced illnesses suffer from a myriad of distressing symptoms. Palliative care aims to alleviate the distress caused by such symptoms. In extreme circumstances palliative sedation may be implemented to manage symptom distress that is not responsive to standard treatment modalities. Nurses are involved in the care of patients receiving palliative sedation as well as their families. To date, however, little research has been conducted examining the nurses' experiences with, and perceptions about the use of palliative sedation in end-of-life care. In order to redress this gap in the literature a descriptive-exploratory study guided by the theory of symbolic interactionism was conducted. Face-to-face interviews were conducted with 10 nurses working on an adult in-patient palliative care unit within a long-term care facility in Canada. The major theme emerging from content analysis of interview transcripts was that of 'Working your way through the quagmire'. The metaphor of the quagmire captured the difficult and complex issues nurses grappled with in instances where palliative sedation was used, and integrates the major categories into the key analytic model emerging from this study.

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 score0.052
Threshold uncertainty score0.636

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 head0.090
GPT teacher head0.466
Teacher spread0.376 · 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

Citations23
Published2006
Admission routes2
Has abstractyes

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