Trends and gaps in climate change and health research in North America
Notice bibliographique
Résumé
The Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (2014) assessed the state of climate change and health knowledge, globally through the Human Health: Impacts, Adaptation, and Co-Benefits Chapter and regionally through chapters, such as the North America Chapter. With IPCC's 6th Assessment Report scheduled to be released in 2021-22, we asked: how has climate change and health research in North America advanced since the IPCC's 5th Assessment Report in 2014? Specifically, we systematically identified and examined trends in the extent, range, and nature of climate-health research conducted in North America. We used a scoping review methodology to systematically identify literature and map publication trends. A search string was used to search five academic databases. Two independent reviewers first screened titles and abstracts, and then the full texts of articles for relevance. Research articles and reviews using systematic methods published since 2013 were eligible for inclusion, and no language restrictions were applied. To be included, articles had to measure and link climatic variables or hazards to health outcomes in North America. Relevant articles were analysed using descriptive statistics to explore publication trends. The number of climate-health articles has significantly increased since the last IPCC Assessment Report. Published research about climate change impacts, heat-related mortality and morbidity, and respiratory illness taking place in urban centres and in the USA continue to dominate the North American climate-health literature, reflected by the high proportion of articles published. Important research gaps on previously neglected climate-sensitive health outcomes, however, are beginning to be filled, including climate change impacts on mental health, nutrition, and foodborne disease. We also observed progress in research that included future projections of climate-health risks; however, projection research is still relatively nascent and under-studied for many climate-sensitive health outcomes in North America, and would benefit from considering social and demographic variables in models. Important research disparities in geographical coverage were noted, including research gaps in Canada and Mexico, and in rural and remote regions. Overall, these publication trends suggest an improved understanding of exposure-response relationships and future projections of climate-health risks for many climate-sensitive health outcomes in North America, which is promising and provides an evidence-base to inform the IPCC 6th Assessment Report. Despite these advancements and considering the urgent policy and practice implications, more research is needed to deepen our understanding of climate-sensitive health outcomes, as well as examine new arising issues that have limited evidence-bases. In particular, transdisciplinary and cross-sector research, that includes the social sciences, examining current and future climate-health adaptation, mitigation, and the adaptation-mitigation nexus should become a top priority for research, given the urgent need for this evidence to inform climate change policies, actions, and interventions.
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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 | 0,056 | 0,138 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,043 | 0,072 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,010 | 0,011 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,003 |
| 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 ».