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Record W4404184710 · doi:10.1111/nin.12683

Nurses' Advocacy in Intensive Care: What Insights Can Nurses' Experiences During the Pandemic Reveal?

2024· article· en· W4404184710 on OpenAlexaff
Carolina da Silva Caram, Elizabeth Peter, Isabela Cancio Velloso, Lílian Cristina Rezende, Bruna Pedroso Canever, Marcelexandra Rabelo, Mara Ambrosina de Oliveira Vargas

Bibliographic record

VenueNursing Inquiry · 2024
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Nursing2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MEDLINEPsychologyMedicinePolitical scienceVirology

Abstract

fetched live from OpenAlex

Patient advocacy must be understood as an ethical component of nursing practice that involves respecting and defending patients' rights and autonomy. During the COVID-19 pandemic, the vulnerability of patients in intensive care units (ICUs) increased requiring that nurses advocate for those patients more than ever in a context in which changes in daily nursing practices of care imposed by the pandemic deeply impacted nurses' advocacy. In this study, we examined ICU nurses' patient advocacy during the pandemic, using feminist ethics as a theoretical lens. Twenty-five ICU nurses from Brazil participated in individual interviews. Our findings reflect that advocacy is a moral component of nursing identity. This moral identity represents the identity of nurses as a profession as it represents their values and responsibilities which are social in nature. Although the pandemic challenged nurses' advocacy practices these professionals had an important role to give voice to patients and to preserve their autonomy and dignity, strengthening patient-centered 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 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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.072
GPT teacher head0.434
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

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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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