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Record W4411085808 · doi:10.25248/reas.e20486.2025

Associação entre o uso de drogas estimulantes e desempenho acadêmico em estudantes de medicina da cidade do Rio de Janeiro

2025· article· pt· W4411085808 on OpenAlexaff
Tamiris Rosa Romer, Yanne Fernanda de Barros Rola, J.A Silva, Aline Cristina Brando Lima Simões

Bibliographic record

VenueRevista Eletrônica Acervo Saúde · 2025
Typearticle
Languagept
FieldSocial Sciences
TopicEvasion and Academic Success Factors
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

Objetivo: Descrever o perfil sociodemográfico de estudantes de medicina no município do Rio de Janeiro e identificar a correlação entre uso de estimulantes cognitivos, desempenho acadêmico, níveis de estresse e saúde mental. Métodos: Estudo transversal, quantitativo, observacional e descritivo com estudantes de medicina do 1º ao 6º ano de universidades particulares do Rio de Janeiro. Um questionário com questões padronizadas foi aplicado online entre maio de 2018 e maio de 2024. Resultados: Dos 169 estudantes, 34,9% relataram usar estimulantes, predominantemente no 3º período. Entre esses, 47,6% perceberam melhora no desempenho acadêmico, 29,8% não notaram diferença e 26,4% estavam indecisos. Além disso, 71,6% dos estudantes relataram aumento na ansiedade após o ingresso no curso, sendo a véspera de provas o período de maior ansiedade para 62,1% dos alunos. Quanto ao uso de bebidas energéticas, 46,1% relataram consumo regular ou ocasional. Conclusão: O uso de estimulantes mostrou efeitos variados no desempenho acadêmico e na saúde mental dos estudantes, destacando a necessidade de maior atenção das universidades aos impactos na ansiedade e no uso de substâncias estimulantes para manter o rendimento acadêmico.

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)0.005
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0030.001
Research integrity0.0020.003
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 head0.031
GPT teacher head0.372
Teacher spread0.341 · 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
Published2025
Admission routes1
Has abstractyes

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