MétaCan
Menu
Back to cohort
Record W4409910357 · doi:10.1590/0034-7167-2024-0075

Nursing care for hospitalized patients with COVID-19 in light of Fundamental Care

2025· article· en· W4409910357 on OpenAlexaff
Fabieli Borges, Elizabeth Bernardino, Camila Rorato, Daniele Cristina dos Reis Bobrowec, Olívia Luciana dos Santos Silva, Amanda Gomes Ribeiro Pujol de Carvalho, Clémence Dallaire

Bibliographic record

VenueRevista Brasileira de Enfermagem · 2025
Typearticle
Languageen
FieldMedicine
TopicPalliative and Oncologic Care
Canadian institutionsUniversité Laval
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsNursingDignityNursing careActive listeningCompassionQualitative researchPsychosocialEmpathyOperationalizationPublic hospitalExploratory researchPsychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: to analyze how the COVID-19 pandemic affected the Fundamental Care provided by nurses to hospitalized patients in a public hospital. METHODS: qualitative, descriptive, exploratory research. Twenty-four nurses were interviewed who cared for patients with COVID-19 in a public hospital in the capital city of the state of Paraná, from January to February 2022. Creswell Content Analysis was applied to the data, operationalized by the MaxQda software and in light of the Fundamental Care theory. RESULTS: three categories were obtained with their respective dimensions: Physical Care (Personal Hygiene; Comfort and Mobilization; Eating and drinking; Rest and sleep; Safety and Medication Management), Psychosocial Care (Communication; Privacy; Dignity, respect and beliefs; Emotional well-being) and Relational Care (Active listening; Empathy and compassion; Engagement, support and involvement with families and caregivers and work with patients). FINAL CONSIDERATIONS: the pandemic period may have provided an opportunity to look at the nurse-patient-care relationship, especially based on the theory of Fundamental 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.028
GPT teacher head0.376
Teacher spread0.348 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueRevista Brasileira de EnfermagemSame topicPalliative and Oncologic CareFrench-language works237,207