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Record W4387414822 · doi:10.1590/0034-7167-2023-0118

Advanced Practice Nursing: “Training” Pillar in Supporting the Proposal in Brazil

2023· article· en· W4387414822 on OpenAlexfundno aff
Cristina Maria Garcia de Lima Pàrada, Elisabete Pimenta Araújo Paz, Lúcia Yasuko Izumi Nichiata, Dulce Aparecida Barbosa, Luciane Prado Kantorski

Bibliographic record

VenueRevista Brasileira de Enfermagem · 2023
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsnot available
FundersUniversidade Estadual do Oeste do ParanáComputer Modelling Group
KeywordsPillarArgument (complex analysis)LegislationTraining (meteorology)Clinical PracticeWork (physics)Nursing practiceMedical educationNursingPolitical scienceMedicine

Abstract

fetched live from OpenAlex

OBJECTIVES: to present the pillars that support what has been called Advanced Practice Nursing and discuss the necessary training for its implementation. METHODS: elements contained in assessment documents for graduate programs proposals, reports of presentations by international professors in countries and selected scientific publications were gathered to compose the argument. RESULTS: practice/competency (adds broad and in-depth knowledge about health processes and scientific evidence, clinical reasoning and clinical skills for therapeutic indications); 3) professional regulation (corresponding legislation and monitoring); and 4) funding (broad training and professional practice policy). FINAL CONSIDERATIONS: the agenda for implementing Advanced Practice Nursing in Brazil involves joining efforts to identify stakeholders for a work to legitimize their importance in the country's health and education overview.

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)0.006
metaresearch head score (Gemma)0.006
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 score0.258
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.091
GPT teacher head0.494
Teacher spread0.404 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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