Are FQHCs the Solution to Care Access for Underserved Children?
Bibliographic record
Abstract
* Abbreviations: ED — : emergency department FQHC — : federally qualified health center PCP — : primary care provider In this issue of Pediatrics , Nath and colleagues1 present an ecological analysis of federally qualified health centers (FQHCs) in California, looking at how Medicaid and uninsured children use emergency departments (EDs). The article is timely because the Affordable Care Act and many state policymakers have bet on FQHCs as the solution to improving health care access for low-income populations while also reducing costs. The central issue, however, is how we ensure that poor children receive the same standard of care as others. FQHCs are health care delivery sites funded by states through federal block grants to provide primary care and related services, such as behavioral health, dental care, and transportation, to low-income and medically underserved communities. From just a few centers, there are now 1202 licensed FQHCs with >10 000 sites serving >20 million persons across the United States.2 This growth is likely to continue for several reasons. First, many pediatricians and other primary care providers (PCPs) either refuse to accept or severely limit Medicaid patients in their panels because of low reimbursement … Address correspondence to Kelly J. Kelleher, MD, MPH, Department of Pediatrics, Nationwide Children’s Hospital, 700 Children’s Dr, FB3145, Columbus, OH 43205. E-mail: kelly.kelleher{at}nationwidechildrens.org
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 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".