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Record W2998442115 · doi:10.17271/19843240122720192236

Conhecimento dos funcionários do serviço de apoio quanto ao descarte de resíduos de serviços de saúde

2019· article· pt· W2998442115 on OpenAlexaff
Fabiana Gonçalves de Oliveira Azevedo Matos, Maria Julia Navarro Kássim, Adnan Navarro de Freitas Kassim

Bibliographic record

VenueRevista Científica ANAP Brasil · 2019
Typearticle
Languagept
FieldMedicine
TopicHealthcare and Environmental Waste Management
Canadian institutionsMonsanto (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsPhilosophy

Abstract

fetched live from OpenAlex

Este trabalho tem por objetivo analisar o conhecimento dos funcionários do serviço de apoio sobre o descarte de resíduos de serviços de saúde. Trata-se de um estudo exploratório, transversal, com análise quantitativa dos dados. A pesquisa foi realizada com funcionários do serviço apoio de um hospital público de ensino localizado na região oeste do Paraná (Brasil). Para a coleta de dados foi construído um instrumento na forma de questionário, contendo perguntas para subsidiar a caracterização da amostra e perguntas fechadas de múltipla escolha que buscavam explorar o tema em questão. A pesquisa foi desenvolvida de acordo com as normas do Conselho Nacional de Saúde, sendo aprovada pelo Comitê de Ética em Pesquisa. Dos 58 (100%) funcionários que faziam parte do serviço de apoio, apenas 22 (34%) fizeram parte do estudo. De forma geral, os resultados se mostraram favoráveis ao bom andamento do serviço, visto que as respostas indicaram que a maioria dos respondentes relataram ter conhecimento sobre o tema explorado e que adotam uma postura adequada diante dos eventos adversos quando identificados na rotina de trabalho. Esse resultado não permite generalizar os achados para todo o grupo, mas permite fazer um diagnóstico real sobre a parcela respondente.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.004

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 head0.024
GPT teacher head0.292
Teacher spread0.268 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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