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Panorama da acreditação (inter)nacional no Brasil

2022· article· pt· W4296374963 on OpenAlexaboutno aff
Júlia Nogueira Treib, Ana María Müller de Magalhães, Silvia Cristina Garcia Carvalho, Victória Gabech Seeger, Amanda da Silveira Barbosa, João Lucas Campos de Oliveira

Bibliographic record

VenueEscola Anna Nery · 2022
Typearticle
Languagept
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

RESUMO Objetivo delinear o panorama da Acreditação nacional e internacional no Brasil. Método estudo descritivo, de abordagem quantitativa e fonte documental. Os campos de inquérito foram as páginas online de acesso irrestrito das seguintes metodologias acreditadoras: Organização Nacional de Acreditação (ONA), Joint Commission International (JCI), Accreditation Canada International (ACI) e QMentum Internacional, além da página do Cadastro Nacional de Estabelecimentos de Saúde (CNES) e/ou sites institucionais. Foram extraídas as variáveis: tipo de instituição/estabelecimento de saúde; regime de gestão setorial; localidade; nível de certificação (em caso de selo concedido pela ONA) e porte (para hospitais). Empregou-se análise estatística descritiva. Resultados apuraram-se os dados de 1.122 certificações, especialmente da ONA (77,2%) e QMentum International (13,2%). Os hospitais prevaleceram na adesão à Acreditação (35,3%), principalmente os de grande porte (60,3%) e do setor privado (75,8%). Houve concentração dos selos de qualidade na região Sudeste do Brasil (64,5%), e a região Norte apresentou menor proporção de estabelecimentos certificados (3%). Conclusões e implicações para a prática as certificações de Acreditação no Brasil remetem à metodologia nacional, com enfoque na área hospitalar privada e na região Sudeste do país. O mapeamento delineado pode sustentar assertividade em políticas de incentivo à gestão da qualidade e avaliação externa no Brasil.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.409
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0320.011

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 head0.136
GPT teacher head0.426
Teacher spread0.290 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations4
Published2022
Admission routes1
Has abstractyes

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