Gender and active travel: a qualitative data synthesis informed by machine learning
dc.contributor.author | Haynes, E | |
dc.contributor.author | Green, J | |
dc.contributor.author | Garside, R | |
dc.contributor.author | Kelly, MP | |
dc.contributor.author | Guell, C | |
dc.date.accessioned | 2020-01-10T12:23:56Z | |
dc.date.issued | 2019-12-21 | |
dc.description.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 '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. Results: Guided 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'). Conclusion: 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. | en_GB |
dc.description.sponsorship | Academy of Medical Sciences and the Wellcome Trust (Springboard - Health of the Public 2040 | en_GB |
dc.description.sponsorship | National Institute for Health Research (NIHR) | en_GB |
dc.identifier.citation | Vol. 16, article 135 | en_GB |
dc.identifier.doi | 10.1186/s12966-019-0904-4 | |
dc.identifier.grantnumber | HOP001\1051 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/40348 | |
dc.language.iso | en | en_GB |
dc.publisher | BMC (part of Springer Nature) | en_GB |
dc.rights | © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. | en_GB |
dc.subject | Active travel | en_GB |
dc.subject | Gender | en_GB |
dc.subject | Machine learning | en_GB |
dc.subject | Text analytics | en_GB |
dc.subject | Qualitative synthesis | en_GB |
dc.title | Gender and active travel: a qualitative data synthesis informed by machine learning | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-01-10T12:23:56Z | |
dc.description | This is the final version. Available from BMC via the DOI in this record. | en_GB |
dc.description | The original transcripts from the primary research studies included in this secondary analysis were only accessible to the authors for the length and use of this project, and are therefore not available to third parties; the corresponding author can be of assistance to liaise with the original institutions which hold the data. | en_GB |
dc.identifier.journal | International Journal of Behavioral Nutrition and Physical Activity | en_GB |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2019-12-16 | |
exeter.funder | ::Academy of Medical Sciences | en_GB |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2019-12-16 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-01-10T12:12:01Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2020-01-10T12:25:05Z | |
refterms.panel | A | en_GB |
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This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.