Bibliographic record
Abstract
At the heart of the COVID-19 pandemic is a long-lasting question about access to healthcare in America. For years, exorbitant drug prices have caused severe problems for American patients. The ineffective system of determining drug costs, solely influenced by competition in the free market, leaves roughly 25% of Americans unable to afford prescription drugs1. Importantly, this emphasizes the urgent need for structural reform in this area. Interestingly, there has been a renewed political will to address this crisis. In May 2018, the Trump Administration released a blueprint to “put American patients first” and, in the report, “high list prices” was the first challenge identified by Health and Human Services2. Six states have already enacted laws that allow for Canadian drug importation but await for federal approval3. These initiatives at the state and federal level, while not amounting to formal legal action, set the stage for paradigm-shifting policies to be passed. The National Academy for State Health Policy (NASHP), a nonpartisan group of policy makers in state governments, represent a key actor for drug price reform4. Recently, they published a model law to match Canadian drug prices for the American market5. The model accounts for the 250 most expensive drugs in the state and proposes setting an upper limit defined by the lowest price found across Canada’s 4 most populous provinces5. In doing so, this aims to decrease drug prices and improve drug accessibility. While not flawless, this model provides an effective starting point to regulate drug prices in America and addresses key critics.
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 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 | 0.022 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.043 | 0.040 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".