Health Care Policy and Congenital Heart Disease: 2020 Focus on Our 2030 Future
Bibliographic record
Abstract
The congenital heart care community faces a myriad of public health issues that act as barriers toward optimum patient outcomes. In this article, we attempt to define advocacy and policy initiatives meant to spotlight and potentially address these challenges. Issues are organized into the following 3 key facets of our community: patient population, health care delivery, and workforce. We discuss the social determinants of health and health care disparities that affect patients in the community that require the attention of policy makers. Furthermore, we highlight the many needs of the growing adults with congenital heart disease and those with comorbidities, highlighting concerns regarding the inequities in access to cardiac care and the need for multidisciplinary care. We also recognize the problems of transparency in outcomes reporting and the promising application of telehealth. Finally, we highlight the training of providers, measures of productivity, diversity in the workforce, and the importance of patient-family centered organizations in advocating for patients. Although all of these issues remain relevant to many subspecialties in medicine, this article attempts to illustrate the unique needs of this population and highlight ways in which to work together to address important opportunities for change in the cardiac care community and beyond. This article provides a framework for policy and advocacy efforts for the next decade.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".