Pediatric and Adult Physician Networks in Affordable Care Act Marketplace Plans
Bibliographic record
Abstract
OBJECTIVES: To describe and compare pediatric and adult specialty physician networks in marketplace plans. METHODS: Data on physician networks, including physician specialty and address, in all 2014 individual marketplace silver plans were aggregated. Networks were quantified as the fraction of providers in the underlying rating area within a state that participated in the network. Narrow networks included none available networks (ie, no providers available in the underlying area) and limited networks (ie, included <10% of the available providers in the underlying area). Proportions of narrow networks between pediatric and adult specialty providers were compared. RESULTS: Among the 1836 unique silver plan networks, the proportions of narrow networks were greater for pediatric (65.9%) than adult specialty (34.9%) networks (P < .001 for all specialties). Specialties with the highest proportion of narrow networks for children were infectious disease (77.4%) and nephrology (74.0%), and they were highest for adults in psychiatry (49.8%) and endocrinology (40.8%). A larger proportion of pediatric networks (43.8%) had no available specialists in the underlying area when compared with adult networks (10.4%) (P < .001 for all specialties). Among networks with available specialists in the underlying area, a higher proportion of pediatric (39.3%) than adult (27.3%) specialist networks were limited (P < .001 except psychiatry). CONCLUSIONS: Narrow networks were more prevalent among pediatric than adult specialists, because of both the sparseness of pediatric specialists and their exclusion from networks. Understanding narrow networks and marketplace network adequacy standards is a necessary beginning to monitor access to care for children and families.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".