1.D. Scientific session: Cross national perspectives: the many faces of health inequities
Notice bibliographique
Résumé
Abstract Health inequities come in different forms in different countries, often times based on country and population demographics, and exist around the world. Ample evidence has revealed how inequities to accessing and utilizing health services can have detrimental consequences for certain populations and exist across numerous strata including geography, income, race and ethnicity, age, and gender. This workshop identifies different examples of inequities across countries, and their impact on health outcomes and health care use. Health inequities, in whichever form, lead to one common outcome: worse health outcomes and lower life expectancy for those who are marginalized by inequities compared to their counterparts. This session will dive into the research on health inequities by geography (rural versus non rural), income (low/average income versus high income), race/ethnicity, and among people with substance use disorder (SUD) particularly those who use and inject drugs with the goal of 1) exposing the stark disparities in health care among these populations; 2) discussing lessons which can be learned and adapted from other countries; and 3) discussing promising solutions and approaches to restoring justice and access to care for all. The panel will be composed of 1 moderator and up to 5 panelists. Panelists are experts in their fields, established researchers and practitioners, and either employees of the Commonwealth Fund or alumni of the Harkness Fellowship from various high-income countries including Canada, Germany, Norway, the United Kingdom, and the United States. A moderator from the Commonwealth Fund will frame the discussion by introducing a deep dive into affordability barriers utilizing the Commonwealth’s Fund International Health Policy Survey of nine countries. Munira Gunja, senior researcher on the Commonwealth Fund’s International program, will present how a lack of affordability in health care is a growing threat and will likely widen the disparities by income without serious health reforms. Neil MacKinnon, Senior Harkness Fellow from Canada, will continue the discussion focusing on an analysis he and his team at Augusta University completed on disparities in health care by geography in eleven (primarily European) countries. The second panelist, Sidra Khan-Gokkaya, the 2023-2024 Harkness Fellow from Germany, will present on her study of racism in health care in Germany and the U.S. Another of the 2023-2024 Harkness Fellows, Claire Wilson, will present on her research of racial disparities in perinatal mental illness in the U.K. and U.S. Lastly, the fifth panelist, Ane Kristine Finbråten will discuss the unique health needs of those with SUD and the inequities faced by people who use and inject drugs as they navigate health care. Each panelist will speak for roughly 8 minutes, with 20 minutes saved for moderator and audience questions. Key messages • All stakeholders, including patients, public health systems, and health systems benefit when all populations are able to access comprehensive health services. • Health inequities come in many different forms, and to improve health outcomes, we must address them at all levels.
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 | 0,016 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,003 |
| Communication savante | 0,011 | 0,008 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,019 | 0,021 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,100 | 0,030 |
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 ».