Gender and active travel: a qualitative data synthesis informed by machine learning

Gender and active travel: a qualitative data synthesis informed by machine learning

Abstract Background Innovative approaches are required to move beyond individual approaches to behaviour change and develop more appropriate insights for the complex challenge of increasing population levels of activity. Recent research has drawn on social practice theory to describe the recursive a...

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Bibliographic Details
Main Authors: Emily Haynes, Judith Green, Ruth Garside, Michael P. Kelly, Cornelia Guell
Format: Article
Language:English
Published: BMC 2019-12-01
Series:International Journal of Behavioral Nutrition and Physical Activity
Subjects:
Active travel
Gender
Machine learning
Text analytics
Qualitative synthesis
Online Access:https://doi.org/10.1186/s12966-019-0904-4

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