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Care management instruments used by nurses in the emergency hospital services

2021· article· en· W3197799481 on OpenAlexaff
Simone Kroll Rabelo, Suzinara Beatriz Soãres de Lima, José Luís Guedes dos Santos, Tanise Martins dos Santos, Emilene Reisdorfer, Daniela Rodrigues Hoffmann

Bibliographic record

VenueRevista da Escola de Enfermagem da USP · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsMacEwan University
Fundersnot available
KeywordsThematic analysisWork (physics)NursingCoping (psychology)Qualitative researchFocus groupTriangulationService (business)PsychologyMedicineBusinessSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the instruments used by nurses for the management of care in face of the demands of the emergency hospital service. METHOD: This is a qualitative study, with triangulation of data from interviews, focus groups, and documents, conducted with nurses from an Emergency Hospital Service in a state in southern Brazil. Data were subjected to thematic content analysis. RESULTS: Seventeen nurses participated in the study. The categories emerging from this study were view of the whole picture, definition of priorities, and physical instruments. These instruments are used by nurses to manage multiple tasks and provide adequate care to patients with different levels of complexity, in the face of an intense and unpredictable work process due to the constant demand for care. CONCLUSION: The instruments used by nurses in their work process are mainly skills and attitudes developed as a coping strategy at an intense and complex work environment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.004
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.402
Teacher spread0.371 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2021
Admission routes1
Has abstractyes

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