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Patient advocacy in valuing social and family being in times of COVID-19

2023· article· en· W4389299988 on OpenAlexaff
Mayara Souza Manoel, Fábio Silva da Rosa, Elizabeth Peter, Carolina da Silva Caram, Kely Regina da Luz, Mara Ambrosina de Oliveira Vargas

Bibliographic record

VenueEscola Anna Nery · 2023
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNursingPandemicIntensive care unitCoronavirus disease 2019 (COVID-19)Exploratory researchIntensive careFamily centered carePsychologyQualitative researchFamily memberMedicineHealth careFamily medicineSociologyPsychiatryPolitical scienceIntensive care medicine

Abstract

fetched live from OpenAlex

Abstract Objective to understand the strategies used by intensive care nurses in the face of situations that required patient advocacy, involving the appreciation of social and family being during the COVID-19 pandemic. Method this is a qualitative, descriptive and exploratory study, carried out in the five regions of Brazil. A total of 25 intensive care nurses participated in the study. Data were collected through a semi-structured interview and subsequently subjected to discursive textual analysis. Results nurses advocated before the health team and for the family’s presence within the Intensive Care Unit. With the COVID-19 pandemic, new strategies were established to advocate, promoting virtual rapprochement between nurses, patients and family members as well as the permanence of family members in intensive care environments when necessary so that nurses could know patients better and integrate the family into care. Conclusion and implications for practice the strategies used to act on behalf of patients were carried out for rapprochement between nurses and family members; for instructing family members to advocate for patients; and for the defense of family presence within the Intensive Care Unit.

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)0.003
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 score0.038
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.109
GPT teacher head0.409
Teacher spread0.299 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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