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

Emily Haynes,Cornelia Guell,Judith Green,Ruth Garside,Michael P Kelly

doi:10.1186/s12966-019-0904-4

Abstract

BackgroundInnovative 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.MethodsWe 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 ‘topic modelling analysis’. Text analytics software, Leximancer, was used in the first phase of the analysis to produce inter-topic distance maps to illustrate inter-related ‘concepts’. The outputs from this first phase guided a second researcher-led interpretive analysis of text excerpts to infer meaning from the computer-generated outputs.ResultsGuided by social practice theory, we identified ‘interrelated’ and ‘relating’ practices across the pooled datasets. For this study we particularly focused on respondents’ commutes, travelling to and from work, and on differentiated experiences by gender. Women largely described their commute as multifunctional journeys that included the school run or shopping, whereas men described relatively linear journeys from A to B but highlighted ‘relating’ practices resulting from or due to their choice of commute mode or journey such as showering or relaxing. Secondly, we identify a difference in discourses about practices across the included datasets. Women spoke more about ‘subjective’, internal feelings of safety (‘I feel unsafe’), whereas men spoke more about external conditions (‘it is a dangerous road’).ConclusionThis 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.

Highlights

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
There is an urgent need for innovative approaches to increase population levels of physical activity, for which interventions have shown only modest success to date [1]
The pooled women’s accounts provided plentiful empirical examples across settings and modes of multifunctional trips that allow them to navigate a complex world with synchronisation and coordination, but limit their choice of travel mode [13]. These accounts were rare within the men’s data, who spoke more about commuting in a linear way. This aligns with existing evidence that such gendered differences do occur and women are more likely to ‘tripchain’ [11, 32], as a consequence of women in couples taking on the responsibility of the household [9, 11], childcare [32, 33] and other unpaid work [34], or as a result of inequalities in the impact of life events on women [15]

Summary

Introduction

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. 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. There is an urgent need for innovative approaches to increase population levels of physical activity, for which interventions have shown only modest success to date [1]. In part, this is because current approaches that aim to enhance physical activity deal poorly with complexity, failing to address the interlinkages and interactions, rather than the mere plurality, of factors and processes. More nuanced research trials and evidence syntheses are called for to explore the independent effect of gender on travel behaviour and the significance of such on policy development [14]

Objectives

Methods

Results

Discussion

Conclusion