A Call for Climate Justice in Medical Curricula
Notice bibliographique
Résumé
To the Editor: Over the last several decades, it has become evident that the biomedical model of health is insufficient for providing equitable care that considers the structural determinants of health and well-being. To deliver patient-centered care, medical students are now being taught the complex interplays between health outcomes, social hierarchies, and various forms of oppression. However, despite the World Health Organization describing climate change as the greatest global health threat of this century, 1 medical school curricula often do not encompass any teachings in this area. Without an appreciation for climate justice, medical students will be unprepared to support patients whose ill health is driven by the oppressive systems underlying climate change. Greenhouse gas emissions have numerous downstream impacts on the environment, the global economy, and human health, which disproportionately affect vulnerable populations such as children, those in racial/ethnic minority groups, and lower-income communities including many in the Global South. 2 According to a 2019 analysis, the health care sector was responsible for 4.4% of annual global carbon emissions. 3 As medicine is firmly grounded in the principle of nonmaleficence, neglecting to address the climate crisis is both unconscionable and paradoxically violates one of the core ethical principles of medicine. To address this gap in medical education, we partnered with the Centre for Sustainable Health Systems at the University of Toronto to host a 6-part webinar series and certificate program on sustainability in medicine. This series had more than 270 registered participants from 8 Canadian provinces. Medical students were well represented among participants, engaging on topics ranging from sustainable practices during a pandemic to advocacy approaches for climate action, and many participants shared ways in which they are advancing the global climate action agenda at their own institutions. Furthermore, a Canadian medical school took notice of our efforts and asked for our assistance in implementing sustainable medicine discussion points in their undergraduate medical curriculum. We are invigorated by the positive response from our colleagues and hope that further trainee-led efforts will continue to inspire ambitious action to build a more equitable future for all on local, national, and international stages. As medical students, we have the power to create change in academic medicine. Throughout our medical school journey, we hope to use our newfound privilege as members of the medical profession to further raise awareness of the health inequities associated with climate change and to better advocate for climate justice. Acknowledgments: The authors thank those at the Centre for Sustainable Health Systems for their guidance and support throughout this process. They also thank Rebecca Wang, a medical student at Temerty Faculty of Medicine, for her assistance in developing the webinar series and certificate program.
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 | 0,014 | 0,076 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,005 |
| Communication savante | 0,008 | 0,007 |
| Science ouverte | 0,005 | 0,005 |
| Intégrité de la recherche | 0,030 | 0,024 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,028 | 0,007 |
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 ».