Networks In ACA Marketplaces Are Narrower For Mental Health Care Than For Primary Care
Bibliographic record
Abstract
There is increasing concern about the extent to which narrow-network plans, generally defined as those including fewer than 25 percent of providers in a given health insurance market, affect consumers' choice of and access to specialty providers-particularly in mental health care. Using data for 2016 from 531 unique provider networks in the Affordable Care Act Marketplaces, we evaluated how network size and the percentage of providers who participate in any network differ between mental health care providers and a control group of primary care providers. Compared to primary care networks, participation in mental health networks was low, with only 42.7 percent of psychiatrists and 19.3 percent of nonphysician mental health care providers participating in any network. On average, plan networks included 24.3 percent of all primary care providers and 11.3 percent of all mental health care providers practicing in a given state-level market. These findings raise important questions about provider-side barriers to meeting the goal of mental health parity regulations: that insurers cover mental health services on a par with general medical and surgical services. Concerted efforts to increase network participation by mental health care providers, along with greater regulatory attention to network size and composition, could improve consumer choice and complement efforts to achieve mental health parity.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".