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

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 relati...

Full description

Saved in:
Bibliographic Details
Published in:The international journal of behavioral nutrition and physical activity 2019-12, Vol.16 (1), p.135-135, Article 135
Main Authors: Haynes, Emily, Green, Judith, Garside, Ruth, Kelly, Michael P., Guell, Cornelia
Format: Article
Language:English
Subjects:
Active travel
Adolescent
Adult
Aged
Analysis
behavior change
Behavioral Sciences
Bubbles
Child
Clinical Nutrition
collective action
computer software
Data collection
Datasets
Exercise
Female
Gender
Gender differences
Health Promotion and Disease Prevention
Humans
issues and policy
Machine Learning
Male
Medicine
Medicine & Public Health
Middle Aged
physical activity
Population
Public health
qualitative analysis
Qualitative Research
Qualitative synthesis
Researchers
Semantics
Sex Distribution
Shopping
Social aspects
Social science research
Software
Studies
Text analytics
Travel
Travel - statistics & numerical data
United Kingdom
Women
Young Adult
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!