Mental health of medical students in Southern Brazil (2023): analysis code

Analysis code for a multicentre cross-sectional survey of 1,558 medical students from all 20 medical schools in Rio Grande do Sul, Brazil (2023). The study estimated the prevalence and co-occurrence of depressive and anxiety symptoms, burnout, thoughts of death or self-harm, non-suicidal self-injury and prescription stimulant misuse, and examined associated factors using modified Poisson regression with cluster-robust variance. The repository contains a complete R replication pipeline, which rebuilds all variables from the raw survey export and automatically checks 1,848 reported estimates, as well as the original Python analysis code. The individual-level data are not included: participants did not consent to public data deposition, and the Research Ethics Committee (CEP-PUCRS, CAAE 62723722.6.0000.5336) did not approve public deposit. The de-identified dataset is available from the corresponding author on reasonable request, subject to ethics committee approval.

Authors

Publication Details

Journal
Open Science Framework
Published
2026-10-26
DOI
https://doi.org/10.17605/osf.io/9h87m
Primary Topic
Healthcare professionals’ stress and burnout
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mental health of medical students in Southern Brazil (2023): analysis code

Lucas Spanemberg
Open Science Framework
Healthcare professionals’ stress and burnout
article

Mental health of medical students in Southern Brazil (2023): analysis code

Lucas Spanemberg
article en

Abstract

Analysis code for a multicentre cross-sectional survey of 1,558 medical students from all 20 medical schools in Rio Grande do Sul, Brazil (2023). The study estimated the prevalence and co-occurrence of depressive and anxiety symptoms, burnout, thoughts of death or self-harm, non-suicidal self-injury and prescription stimulant misuse, and examined associated factors using modified Poisson regression with cluster-robust variance. The repository contains a complete R replication pipeline, which rebuilds all variables from the raw survey export and automatically checks 1,848 reported estimates, as well as the original Python analysis code. The individual-level data are not included: participants did not consent to public data deposition, and the Research Ethics Committee (CEP-PUCRS, CAAE 62723722.6.0000.5336) did not approve public deposit. The de-identified dataset is available from the corresponding author on reasonable request, subject to ethics committee approval.

Open Science Framework
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Openalex Percentile: Top 9%
Healthcare professionals’ stress and burnout
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon