1.D. Scientific session: Cross national perspectives: the many faces of health inequities
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.100 | 0.030 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".