MétaCan
Menu
Back to cohort
Record W3041404491 · doi:10.1590/0034-7167-2018-0923

Nurses' work process in an emergency hospital service

2020· article· en· W3041404491 on OpenAlexaff
Simone Kroll Rabelo, Suzinara Beatriz Soãres de Lima, José Luís Guedes dos Santos, Valdecir Zavarese da Costa, Emilene Reisdorfer, Tanise Martins dos Santos, Jocelaine Cardoso Gracióli

Bibliographic record

VenueRevista Brasileira de Enfermagem · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsCentennial College
Fundersnot available
KeywordsThematic analysisWork (physics)Content analysisExploratory researchService (business)NursingCentralityDimension (graph theory)Qualitative researchFocus groupProcess (computing)Descriptive statisticsMedicineMedical emergencyBusinessComputer scienceSociologyEngineering

Abstract

fetched live from OpenAlex

OBJECTIVES: to analyze the nurses' work process in an Emergency Hospital Service. METHODS: a qualitative, exploratory and descriptive research conducted with 17 nurses from the emergency service of a high complexity hospital in southern Brazil. Data were collected through interviews, focus group and document analysis. Data analysis followed the thematic content analysis framework. RESULTS: four categories emerged: Work environment characteristics; Assistance dimension; Management dimension; Care management. Final Considerations: the nurses' work process in Emergency Hospital Service is characterized by the peculiarities of the setting, with centrality in care and care management aiming at quality care and safety to patients.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
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 head0.093
GPT teacher head0.445
Teacher spread0.352 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueRevista Brasileira de EnfermagemSame topicHealth, Nursing, Elderly CareFrench-language works237,207