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Record W3174851547 · doi:10.1111/nin.12437

Beyond technology, drips, and machines: Moral distress in PICU nurses caring for end‐of‐life patients

2021· article· en· W3174851547 on OpenAlexaffabout
Michelle Gagnon, Diane Kunyk

Bibliographic record

VenueNursing Inquiry · 2021
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEnd-of-life careDignityBioethicsNursingDistressPediatric intensive care unitLife supportIntensive carePalliative carePsychologyIntensive care unitMedicinePsychiatryIntensive care medicinePsychotherapistPolitical science

Abstract

fetched live from OpenAlex

Moral distress is an experience of profound moral compromise with deeply impactful and potentially long-term consequences to the individual. Critical care areas are fraught with ethical issues, and end-of-life care has been associated with numerous incidences of moral distress among nurses. One such area where the dichotomy of life and death seems to be at its sharpest is in the pediatric intensive care unit. The purpose of this study was to understand the moral distress experiences of pediatric intensive care nurses when caring for pediatric patients at the end of life. A secondary analysis was undertaken of seven transcripts from registered nurses across six Canadian pediatric intensive care units and produced three themes: under prioritization of child patient dignity, burden of insider knowledge, and environmental constraints on nursing roles and responsibilities. When caring for patients at the end of life, nurses experienced moral distress when a dignified death was not realized. Furthermore, despite interprofessional collaboration efforts in Canada, the concept of silo mentality persists and contributes to moral distress. Organizational involvement is needed to address moral distress in pediatric intensive care nurses both to achieve a dignified death for child patients and in addressing silo mentality.

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)0.001
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.087
GPT teacher head0.467
Teacher spread0.380 · 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.

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

Citations30
Published2021
Admission routes2
Has abstractyes

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