Advancing health equity in cardiovascular care
Bibliographic record
Abstract
Unacceptable and complex yet also often unintended and unexpected health inequities exist in cardiovascular care and outcomes.While prevalent across health care, professionals involved in cardiovascular health care should know and address the sources of these inequalities in practice and policy.This editorial provides a primer to do so. Health equityHealth equity is a principle underlying a commitment to reduce and even eliminate downstream disparities in and upstream determinants of health, including social and economic ones.Pursuing health equity means striving for the highest possible standard of health for all people, especially those at greatest risk of poor health outcomes.Accordingly, health equity is the state in which every person has a fair and just opportunity to realize their highest level of health. 1 Unlike health equality, which refers to equal health care for everyone, health equity aims to adjust healthcare resources based on need.Thus, equity refers to fairness in healthcare outcomes regardless of any social determinants of health. 1 Multiple determinants of healthKey social determinants of cardiovascular health include socioeconomic status, race and ethnicity, social support, culture and language, access to care, and residential environment. 2This also aligns with the notion that health has multiple determinants, including but beyond social determinants, such as genetic, behavioural, environmental, and physical factors. 2,3ccordingly, the health of people and communities is influenced by a myriad of physical, social, and economic conditions, including highly interrelated, clustered, and compounding social, cultural, and structural factors affecting individuals and communities.For example, educational attainment is strongly linked to individual and neighbourhood deprivation and low health literacy. 4People from low socioeconomic areas are more likely to undertake behaviour and have risk factors associated with cardiovascular disease, such as physical inactivity, smoking, diabetes, hypertension, and a high body mass index. 4,5Given upstream factors such as neighbourhood and household poverty and unequal access to education and health care are also associated with premature cardiovascular disease, patients from low socioeconomic areas, while at highest risk from multiple determinants, are ironically less likely to access and benefit from effective health care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".