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Record W4386295048 · doi:10.38087/2595.8801.295

TRABALHO REMOTO E AS DESORDENS MUSCULOESQUELÉTICAS: UMA REVISÃO INTEGRATIVA DA LITERATURA

2023· article· pt· W4386295048 on OpenAlexaff
Danielle Aprígio, Rondineli de Jesus Barro, Ana Carolina Marcelino Chermout, Daiana da Silva Santos Muquim, R. C. Ferreira, Thalia Sant’Anna de Freitas Gomes Soares

Bibliographic record

VenueCOGNITIONIS Scientific Journal · 2023
Typearticle
Languagept
FieldHealth Professions
TopicOccupational Health and Safety in Workplaces
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsHumanitiesPsychologyPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Introdução: A globalização, comunicação e tecnologia impactaram aspectos sociais, culturais, políticos e econômicos nos últimos anos. Com essas transformações surgiram novas formas de trabalho, como o teletrabalho que utiliza de tecnologia de informação para o trabalho fora da empresa. Com isso, observam-se mudanças nas relações entre o trabalho e o indivíduo. Objetivos: Sintetizar evidências que relacionam as desordens musculoesqueléticas ao teletrabalho. Analisar os fatores de riscos organizacionais e ergonômicos, e, propor a adoção de medidas preventivas, de controle aos fatores agravantes a saúde do teletrabalhador. Metodologia: Trata-se de uma revisão da literatura do tipo integrativa, foram utilizados estudos extraídos das bases de dados: Pubmed/Medline, ScieLO, Lilacs e PEDro, publicados entre 2010 à 2023. Conclusão: O teletrabalho pode causar impactos físicos e psicológicos. Problemas musculoesqueléticos e ergonômicos são frequentes. A ergonomia e a organização adequada do ambiente são fundamentais para prevenir tais problemas. Pausas regulares e exercícios físicos podem diminuir tais impactos.

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)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, 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: Empirical
Teacher disagreement score0.117
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.005
Science and technology studies0.0150.001
Scholarly communication0.0040.001
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0140.032

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 head0.103
GPT teacher head0.445
Teacher spread0.342 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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