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Hospital work process and burnout syndrome among nursing professionals

2020· article· pt· W3044500423 on OpenAlexaff
Amina Silva, Amanda Nicácio Vieira, María Itayra Padilha, Stéfany Petry

Bibliographic record

VenueRevista de Pesquisa Cuidado é Fundamental Online · 2020
Typearticle
Languagept
FieldHealth Professions
TopicOccupational Health and Burnout
Canadian institutionsQueen's University
FundersUniversidade Federal de Santa Catarina
KeywordsBurnoutSciELONursingPsychologyMedicineMEDLINEHumanitiesClinical psychologyPolitical science

Abstract

fetched live from OpenAlex

Objetivo: Identificar os fatores do processo de trabalho que favorecem o desenvolvimento da Síndrome de Burnout em profissionais de enfermagem na assistência hospitalar. Método: Trata-se de uma revisão integrativa de literatura, realizada nas bases de dados LILACS, SciELO, MEDLINE e BDENF, obedecendo as etapas metodológicas propostas por Ganong. Resultados: Foram selecionados 11 artigos científicos para compor esta revisão. Estes refletiram sobre a influência diante do aparecimento da Síndrome de Burnout em profissionais de enfermagem, sendo destacadas em quatro categorias: Fatores que desencadeiam a exaustão emocional; Fatores que desencadeiam a despersonalização; Fatores que desencadeiam a baixa satisfação no trabalho e outros fatores facilitadores da Síndrome de Burnout. Conclusão: Os fatores desencadeadores dessa síndrome podem ser evitados de forma geral, oferecendo ao profissional de enfermagem; suporte psicológico, melhoria das condições de trabalho e o exercício dos seus direitos, de modo a exercer sua função em condições físicas e mentais.

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)0.004
metaresearch head score (Gemma)0.017
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.405
Teacher spread0.367 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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