When Patients Lose Coverage, Clinicians Lose Heart
Bibliographic record
Abstract
Recent federal legislation is likely to strip 16 million people in the United States of health care coverage (Ortaliza et al., 2025).Decades of research confirm that access to care is a cornerstone of good health, and the consequences of widespread coverage loss could be staggering.Uninsured adults experience higher mortality rates, reduced control of chronic conditions, and a greater number of preventable hospitalizations than their insured peers, all of which place a strain on the health care system and the economy overall (IOM, 2002; IOM, 2009).Much has been written about the likely conse quences of cuts to federal health programs for patients-and rightly so, given that they are significant in scale.Importantly, attention must be paid to understand the interlinked con se quences for members of the health workforce.The authors of this commentary believe there is a real risk that widespread un and underinsurance could cause significant care challenges and moral distress for clinicians-and thereby contribute to difficulties in providing care as well as a crisis of burnout that is already straining the care system.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 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".