Access Points for the Underserved
Bibliographic record
Abstract
BACKGROUND: Federally Qualified Health Centers (FQHCs) are a vital source of primary care for underserved populations, such as Medicaid enrollees and the uninsured. Their role in delivering care may increase through new funding allocations in the Affordable Care Act and expanded Medicaid programs across many states. OBJECTIVE: Examine differences in appointment availability and wait-times for new patient visits between FQHCs and other providers. RESEARCH DESIGN: We use experimental data from a simulated patient study to compare new patient appointment rates across FQHC and non-FQHC practices for 3 insurance types (private, Medicaid, and self-pay). Trained auditors, posing as patients requesting the first available new patient appointment, were randomized to call primary care providers in 10 states in late 2012 and early 2013. Multivariate regression models adjust for caller-level, clinic-level, and area-level variables. STUDY SETTING: The sample comprises 10,904 calls, including 544 calls to FQHCs. RESULTS: FQHCs grant new patient appointments at high rates, irrespective of patient insurance status. Adjusting for caller, clinic, and area variables, the Medicaid appointment rate at FQHCs is 22 percentage points higher than other primary care practices. Although the appointment rate difference between FQHCs and non-FQHCs is somewhat smaller for the self-pay group, FQHCs are much more likely to provide a lower-cost visit to these patients. Conditional on receiving an appointment, wait-times at FQHCs are comparable with other providers. CONCLUSION: FQHCs' greater willingness to accept new underserved patients before 2014 underscores their potential key roles as health reform proceeds.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.072 | 0.005 |
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".