Gender and active travel: a qualitative data synthesis informed by machine learning.
Emily HaynesJudith GreenRuth GarsideMichael P KellyCornelia GuellPublished in: The international journal of behavioral nutrition and physical activity (2019)
This rare application of machine learning to qualitative social science research has helped to identify potentially important differences in co-occurrence of practices and discourses about practice between men's and women's accounts of travel across diverse contexts. These findings can inform future research and policy decisions for promoting travel-related social practices associated with increased physical activity that are appropriate across genders.
Keyphrases
- healthcare
- machine learning
- mental health
- primary care
- physical activity
- big data
- public health
- artificial intelligence
- infectious diseases
- polycystic ovary syndrome
- body mass index
- deep learning
- electronic health record
- current status
- systematic review
- middle aged
- sleep quality
- metabolic syndrome
- breast cancer risk
- adipose tissue
- skeletal muscle
- data analysis
- cervical cancer screening