Prevalência de sintomas de estresse nos estudantes de medicina
Bibliographic record
Abstract
OBJETIVO: Identificar o perfil sociodemográfico dos acadêmicos de Medicina e avaliar a prevalência de sintomas de estresse nesses estudantes. MÉTODOS: A população estudada respondeu ao Inventário de Sintomas de Stress para Adultos de Lipp (ISSL), para identificação de sintomas de estresse e a um questionário sociodemográfico. RESULTADOS: Duzentos estudantes de Medicina, matriculados nos Ciclos Básico ou Clínico da Universidade Federal do Ceará (UFC), foram estudados. Houve predominância do sexo masculino (54,5%), solteiros (100%), naturais de Fortaleza (87%), com idade média de 21 (± 2,3) anos. As alunas apresentaram níveis de sintomas de estresse maiores do que os estudantes homens, representando 30,1% e 19,6%, respectivamente (p < 0,001). A prevalência de sintomas de estresse foi de 49,7%, sendo o quarto semestre o período com níveis mais altos desses sintomas (p = 0,002). CONCLUSÕES: Conclui-se que estudantes de Medicina da Universidade Federal do Ceará (UFC) têm níveis de estresse similares àqueles relatados para amostras internacionais.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".