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 and relational character of active living but to date most evidence is limited to small-scale qualitative research studies. To 'upscale' insights from individual contexts, we pooled data from five qualitative studies and used machine learning software to explore gendered patterns in the context of active travel.

Methods

We drew on 280 transcripts from five research projects conducted in the UK, including studies of a range of populations, travel modes and settings, to conduct unsupervised '

SUBMITTER: Haynes E 

PROVIDER: S-EPMC6925863 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature