Challenges in Promoting Health Equity and Reducing Disparities in Access Across New and Established Technologies
Bibliographic record
Abstract
Medical innovations and novel technologies stand to improve the return on high levels of health spending in developed countries, particularly in cardiovascular care. However, cardiac innovations also disrupt the landscape of accessing care, potentially creating disparities in who has access to novel and extant technologies. These disparities might disproportionately harm vulnerable groups, including those whose nonmedical conditions-including social determinants of health-inhibit timely access to diagnoses, referrals, and interventions. We first document the barriers to access novel and existing technologies in isolation, then proceed to document their interaction. Novel cardiac technologies might affect existing available services, and change the landscape of care for vulnerable patient groups who seek access to cardiology services. There is a clear need to identify and heed lessons learned from the dissemination of past innovations in the development, funding, and dissemination of future medical technologies to promote equitable access to cardiovascular care. We conclude by highlighting and synthesizing several policy implications from recent literature.
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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 | 0.084 | 0.171 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".