100,000 records from tech workers include burnout and clinical mental health screening scores from PHQ-9 and GAD-7 instruments. The data covers 12 distinct job roles and is described as research-backed. Three machine learning targets are defined, suggesting suitability for predictive modeling tasks.
Use Cases
- Predict burnout risk based on PHQ-9 and GAD-7 screening scores.
- Analyze mental health prevalence across 12 different tech industry roles.
- Build classification models for the three defined machine learning targets.
Strengths
- 100,000 records provide a substantial sample size for analysis.
- Includes research-backed clinical screening instruments (PHQ-9, GAD-7).
- Covers 12 distinct job roles for comparative analysis.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Data may reflect geographic, temporal, or source bias inherent to Kaggle.