Mental health disorders and their impact on cardiovascular health disparities
Bibliographic record
Abstract
Mental health disorders are highly prevalent and are associated with significant morbidity, disability, and reduced life expectancy. A key contributor to this disparity is the increased risk of cardiovascular disease (CVD), which is partially driven by inequalities in social determinants of health, healthcare access, and quality of care. To address this challenge, The Lancet Regional Health-Europe convened experts to evaluate the current state of knowledge on inequalities and disparities in cardiovascular health among people with mental health disorders and propose recommendations to address these disparities. This Series paper aims to raise awareness of the disparities in CVD and health-care quality faced by individuals with common mental health conditions such as major depression, anxiety disorders, schizophrenia, bipolar disorder, and posttraumatic stress disorder. There is an urgent need for increased investment, intervention, and research to address the burden of CVD in these populations. Effective management of the comorbidity between mental health disorders and CVD requires an integrated and holistic approach to clinical care that addresses shared risk factors and the complex interactions between physical and mental health.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".