Ten health policy challenges for the next 10 years
Bibliographic record
Abstract
Abstract Health policies and associated research initiatives are constantly evolving and changing. In recent years, there has been a dizzying increase in research on emerging topics such as the implications of changing public and private health payment models, the global impact of pandemics, novel initiatives to tackle the persistence of health inequities, broad efforts to reduce the impact of climate change, the emergence of novel technologies such as whole-genome sequencing and artificial intelligence, and the increase in consumer-directed care. This evolution demands future-thinking research to meet the needs of policymakers in translating science into policy. In this paper, the Health Affairs Scholar editorial team describes “ten health policy challenges for the next 10 years.” Each of the ten assertions describes the challenges and steps that can be taken to address those challenges. We focus on issues that are traditionally studied by health services researchers such as cost, access, and quality, but then examine emerging and intersectional topics: equity, income, and justice; technology, pharmaceuticals, markets, and innovation; population health; and global health.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".