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Record W4291227876 · doi:10.1111/nuf.12786

Moral distress in critical care nursing practice: A concept analysis

2022· review· en· W4291227876 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNursing Forum · 2022
Typereview
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsWestern University
Fundersnot available
KeywordsNursingPsychologyFormal concept analysisNursing practiceNursing careMedicineComputer science

Abstract

fetched live from OpenAlex

AIM: To provide a critical analysis of the concept of moral distress (MD) in critical care (CC) nursing. BACKGROUND: Despite extensive inquiry pertaining to the legitimacy of MD within nursing discourse, some authors still question its relevancy to the profession. However, amid the global COVID-19 pandemic, MD is generating a significant amount of discussion anew, warranting the further exploration of the concept within CC nursing to provide clarity and expand on the definition. DESIGN: Rodger's Evolutionary Concept Analysis method was used to guide this analysis. METHODS: Related terms, attributes, antecedents, and consequences of MD were identified using current literature. RESULTS: The results of this analysis demonstrate strong congruence between the attributes, antecedents, and negative consequences pertaining to MD. However, a new theme has emerged from this review of the contemporary literature, highlighting the potential unexpected positive outcomes perceived by nurses who experience MD, including the provision of better care, increased levels of empathy, and enhanced opportunities for ethical reflection.

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.

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)0.004
metaresearch head score (Gemma)0.045
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.879
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.021
Insufficient payload (model declined to judge)0.0030.000

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 head0.285
GPT teacher head0.648
Teacher spread0.362 · 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