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Record W4387218138 · doi:10.34119/bjhrv6n5-288

O aumento dos casos da Síndrome de Burnout nos profissionais da saúde durante a pandemia da Covid-19

2023· article· pt· W4387218138 on OpenAlexaff
Guilherme Gusmão Peña, João Vitor Vieira Pontes, José Murilo Dantas Dos Santos, Lucas Bronzeado Cavalcanti Coutinho, Lucas Ruan da Silva Sefer, Marina Martorella Lima, Daniela Heitzmann Amaral Valentin De Sousa, Isabela Tatiana Sales de Arruda

Bibliographic record

VenueBrazilian Journal of Health Review · 2023
Typearticle
Languagept
FieldComputer Science
TopicHealthcare during COVID-19 Pandemic
Canadian institutionsDalsa Corporation
Fundersnot available
KeywordsHumanitiesBurnoutCoronavirus disease 2019 (COVID-19)MedicineProfessional psychologyPhilosophyInternal medicine

Abstract

fetched live from OpenAlex

Com o início do período pandêmico pela COVID-19 no início de 2020, os profissionais das mais variadas áreas tiveram que se adaptar ao novo momento, adotando como medida preventiva o "home office". Entretanto, os trabalhadores do setor da saúde não são detentores desse privilégio, uma vez que são a classe laboral mais requisitada em uma época de crise sanitária. Além disso, o maior problema é que seu trabalho envolve o contato físico frequente com os pacientes, o que expõe essas pessoas à contaminação. Não apenas isso, mas há estudos que descrevem a possibilidade do aumento dos índices da síndrome de burnout – distúrbio de esgotamento profissional – entre profissionais da área de saúde, uma vez que, devido à alta procura por seus serviços, suas cargas horárias estão mais elevadas. Ainda, é importante destacar o surgimento de burnout associado a sinais psíquicos, a saber do estresse causado pelo elevado número de mortes e da preocupação de contaminação de familiares por parte desses empregados. A partir disso, o estudo é uma revisão literária feita a partir da base de dados PubMed e Scielo com o objetivo de analisar o aumento dos casos da síndrome de burnout nos profissionais da saúde durante a pandemia da COVID-19.

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)0.024
metaresearch head score (Gemma)0.005
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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0040.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.001

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 head0.112
GPT teacher head0.432
Teacher spread0.321 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2023
Admission routes1
Has abstractyes

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Same venueBrazilian Journal of Health ReviewSame topicHealthcare during COVID-19 PandemicFrench-language works237,207