Applying Digital Technology to Understand Human Experiences of Climate Change Impacts on Food Security and Mental Health: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: The global impact of climate change ranges from intense heatwaves to extreme weather events that endanger entire ecosystems and people's way of life. Adverse climate change events place undue stress on food and health systems, with consequences for human food security and mental health status. Ubiquitous digital devices, such as smartphones, have the potential to manage existing and emerging climate-related crises, given their ability to enable rapid response, instant communication, and knowledge sharing. OBJECTIVE: This scoping review aimed to identify digital apps being used to capture or address climate change impacts on food security and mental health to inform the development of a digital citizen science initiative. METHODS: A scoping review was conducted using 3 peer-reviewed databases (PubMed, IEEE Xplore, and Web of Science) and manual gray literature searches of relevant organizational (ie, governmental and nonprofit) websites to identify articles and reports published between January 2012 and July 2023. Three separate searches were conducted in each database to identify digital apps focused on climate change and (1) food security, (2) mental health, and (3) food security and mental health. Two reviewers conducted initial screening, with a third reviewer resolving any discrepancies. Articles focused on climate change impacts on wildlife or agriculture (ie, not human food security) were excluded. Full-text screening was conducted for shortlisted articles, and a final data abstraction table was generated, summarizing key app features, contextual factors, and participant involvement. RESULTS: From the 656 records screened, 14 digital apps met the inclusion criteria. The food security apps (n=7, 50%) aimed to capture traditional knowledge to preserve food systems, conduct food security assessments, and aid users in decreasing food insecurity risk. The mental health apps (n=7, 50%) assessed climate change-related stress and provided users with coping strategies following adverse weather events. No digital apps examined the intersection of climate change, food security, and mental health. Key app features included user-to-user communication (n=5, 36%), knowledge databases (n=5, 36%), data collection and analysis (n=3, 21%), gamification (n=1, 7%), and educational resources (n=2, 14%) to address climate change impacts on food security or mental health. In total, 3 approaches to participant involvement were used across studies, including contributory (n=1, 7%), collaborative (n=1, 7%), and cocreative (n=1, 7%) approaches, to ensure the relevance and use of digital apps. CONCLUSIONS: Most digital apps identified provided a service to citizens to either prevent adverse climate change-related health impacts or manage these effects following an acute event or a natural disaster. The capacity of ubiquitous digital tools to enable near real-time communication, the involvement of various stakeholder groups, and their ability to share relevant educational resources in a timely manner are important for developing tailored climate change adaptation and mitigation strategies across jurisdictions.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,067 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,017 | 0,014 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».