Structured Code Review in Mental Health Research

Errors in scientific code pose a significant risk to the accuracy of research. Yet, formal code review remains uncommon in academia. This is a particular challenge for interdisciplinary fields, such as mental health research, which increasingly rely on computational approaches. This paper presents a pragmatic experience-based framework for code review procedures in mental health research. In order to facilitate practical implementation, it includes a structured checklist for identifying common coding issues, from data handling errors to flawed statistical analyses. We discuss barriers to introducing code review and how to overcome them, revisit best practices from software engineering, and highlight the emerging role of large language models in automating aspects of code review. We argue that, despite perceived costs, code review significantly enhances the reliability of research results and fosters a culture of transparency and continuous learning. Our proposal provides an adaptable model for integrating code review into research workflows, helping mitigate errors before publication and strengthening trust in scientific results.