Development and validation of PubMed and Ovid MEDLINE search filters for exposure pathways linking climate change with human health
Notice bibliographique
Résumé
ABSTRACT Introduction Climate change (CC) has major public and global health impacts to which policymakers need to respond. High-quality evidence syntheses (ES) are essential for policy-making. Search filters - validated combinations of search terms - play an important role in implementing robust search methods for ES. The identification of climate-health evidence presents challenges, such as the volume and multidisciplinary nature of the evidence and the fact that relevant studies do not consistently state their link to CC. Thus, our aim was to develop search filters for two search interfaces of the MEDLINE database. Methods CC impacts human health via several exposure pathways: extreme weather events, heat stress, air quality, water quality and quantity, food supply and safety, vector distribution and ecology, and social factors. We established a gold standard by comprehensively identifying health-related ES mentioning CC in five literature databases in February 2021. After screening 8,614 search results, we identified 110 ES for inclusion, extracted their included studies, and classified them according to exposure pathways. From this gold standard we empirically derived search terms per pathway and tested their performance with an independent set of studies. Results We extracted 2,324 studies from the first 79 ES. Based on a gold standard with 1,572 relevant studies indexed in PubMed, it was possible to develop and validate search filters with a sensitivity of 95%, 97% and 99% for six of the seven major climate-health exposure pathways. Filter development was not possible for one pathway due to the lack of coverage in MEDLINE. Conclusion We designed ready-to-use PubMed and Ovid MEDLINE search filters with a graded sensitivity for most exposure pathways linking CC with human health. These can be deployed by public and global health researchers conducting ES or primary research on climate-health to ensure robust identification of relevant evidence. KEY MESSAGES What is already known on this topic : The identification of evidence linking human health with climate change in literature databases such as MEDLINE presents challenges. Empirically derived search filters, which are validated combinations of search terms that can be readily used, are lacking for this topic. What this study adds : We present search filters with a graded sensitivity for six of the seven major climate-health exposure pathways (air quality, extreme weather events, food supply and safety, heat stress, vector distribution and ecology, water quality and quantity). A search filter for the pathway ‘social factors’ was not viable, suggesting that it requires other databases and complementary search methods to be used. How this study might affect research, practice or policy : The new search filters will help health researchers to identify relevant studies with a relationship to climate change. The filters can be applied independently from specific research questions (interventions, prognosis, associations, impacts, diseases, populations, or regions) as they focus on the major exposure pathways linking health with climate change.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,203 | 0,522 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,013 | 0,016 |
| Bibliométrie | 0,076 | 0,038 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,007 | 0,009 |
| Science ouverte | 0,006 | 0,008 |
| Intégrité de la recherche | 0,005 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,003 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».