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Record W2920645900 · doi:10.1111/nin.12290

Explorations of disgust: A narrative inquiry into the experiences of nurses working in palliative care

2019· article· en· W2920645900 on OpenAlexaff
Mara Kaiser, Helen Kohlen, Vera Caine

Bibliographic record

VenueNursing Inquiry · 2019
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsUniversity of Alberta HospitalUniversity of Alberta
Fundersnot available
KeywordsDisgustNarrativePsychologyPalliative careNarrative inquiryNursingMedicineSocial psychologyAngerPhilosophy

Abstract

fetched live from OpenAlex

While feelings of disgust and repulsion are experienced and accepted as part of care practices of nurses who work in palliative care, they are often silenced. Working alongside two palliative care nurses in a hospice setting, we engaged in a narrative inquiry to inquire into their experiences of disgust. The study took place in a palliative care setting in a large urban city in Germany. We understand care practices as actions that follow a logic of care. According to a logic of care, actions are situated within a social context, given by specific relationships including power, and individual needs. Various aspects of disgust are visible in the experiences of the participants and highlighted in the narrative threads of disgust and silence, disgust and protection, and disgust and boundaries. Embedded in the experience of disgust of nurses working in palliative care, we see that there are borderlands of care that challenge who we are and are becoming. Opening discussion about disgust in nursing makes visible the complexity of care.

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0130.028
Scholarly communication0.0110.010
Open science0.0020.015
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0020.000

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 head0.065
GPT teacher head0.385
Teacher spread0.320 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations21
Published2019
Admission routes1
Has abstractyes

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