Inadequate in the Best of Times: Reevaluating Provider Networks in Light of the Coronavirus Pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The coronavirus has affected billions of people worldwide. As of early June, estimates of infections exceeded six million individuals, about double the number from early May. The United States has experienced more cases than Spain, Italy, France, the United Kingdom, Germany, Turkey, Canada, Japan, and Russia combined. To make things worse, the structure of the U.S. health-care system may significantly impede access to needed medical services while exposing patients to financial liabilities. One particularly concerning feature may be the limitations on access imposed by provider networks. This article briefly reviews what we know about the narrowing of provider networks, and how findings from a series of recent articles illustrating the often-severe restrictions imposed by these networks may be particularly detrimental in the middle of a global health emergency. I also highlight how the actions taken by policymakers to temporarily mitigate these problems have fallen short and what potential long-term solutions might look like.
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.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it