Extended Materials for Protocol: Mapping global research on climate and health using machine learning (systematic protocol) v.2
Creators: name: Berrang-Ford, Lea
name: Sietsma, Anne J.
name: Callagham, Max
name: Minx, Jan
name: Scheelbeek, Pauline
name: Haddaway, Neal R.
name: Haines, Andy
name: Belesova, Kristine
name: Dangour, Alan D.
Publisher: name: Zenodo
Year: 2020
Subjects: subject: climate change
subject: health
subject: machine learning
subject: systematic review
subject: evidence synthesis
subject: protocol
subject: climate
subject: http://id.nlm.nih.gov/mesh/D002980
subject: http://id.nlm.nih.gov/mesh/D004778
subject: http://id.nlm.nih.gov/mesh/D057231
subject: http://id.nlm.nih.gov/mesh/D000069550
subject: http://id.nlm.nih.gov/mesh/D012196
subject: http://id.nlm.nih.gov/mesh/D000078182

description: This document provides extended materials (similar to Supplemental Materials or an Appendix) to the paper, <em>Mapping global research on climate and health using machine learning (systematic protocol)</em>, including: 1. Detailed screening and tagging criteria used for document screening and coding 2. Detailed scope of inclusions &amp; exclusions 3. ROSES Systematic Mapping checklist The full protocol paper (<em>Mapping global research on climate and health using machine learning (systematic protocol)) </em>is submitted to <em>Wellcome Open Research</em>. The abstract of the full protocol is below: <em>Background:</em> Climate change is already affecting health in populations around the world, threatening to undermine the past 50 years of global gains in public health. Health is not only affected by climate change via many causal pathways, but also by the emissions that drive climate change and their co-pollutants. Yet there has been relatively limited synthesis of key insights and trends at a global scale across fragmented disciplines. Compounding this, an exponentially increasing literature means that conventional evidence synthesis methods are no longer sufficient or feasible. Here, we outline a protocol using machine learning approaches to systematically synthesize global evidence on the relationship between climate change, climate variability, and weather (CCVW) and human health. <em>Methods: </em>We will use supervised machine learning to screen over 300,000 scientific articles combining terms related to CCVW and human health. Our inclusion criteria comprise articles published between 2013 and 2020 that focus on empirical assessment of: CCVW impacts on human health or health-related outcomes or health systems; relate to the health impacts of mitigation strategies; or focus on adaptation strategies to the health impacts of climate change. We will use supervised machine learning (topic modeling) to categorize included articles as relevant to impacts, mitigation, and/or adaptation, and extract geographical location of studies. Unsupervised machine learning using topic modeling will be used to identify and map key topics in the literature on climate and health, with outputs including evidence heat maps, geographic maps, and narrative synthesis of trends in climate-health publishing. To our knowledge, this will represent the first comprehensive, semi-automated, systematic evidence synthesis of the scientific literature on climate and health.
description: This document provides the Extended Materials only. The full Protocol is available via Wellcome Open Research. This second version includes the addition of detailed information on the scope of inclusions and exclusions.