Facts and Figures on the ACA in California: What We've Gained and What We Stand to Lose
Bibliographic record
Abstract
The 2016 election of Donald Trump as president, combined with a Republican-controlled Congress, could result in a partial or complete repeal of the Affordable Care Act (ACA). Here is a reminder of what has been achieved under the ACA -- and what California now stands to lose.Nationally, the uninsured rate is at a historic low:20 million Americans are covered as a result of the ACA.Close to 90% of all Americans are now covered: The uninsured rate fell from 16.6% in 2013 to a historic low of 10.5% in 2015.Some of the ACA's biggest gains have been in California:Over 5 million Californians have insurance as a result of the ACA -- roughly a quarter of all Americans covered under the law.91% of Californians are now insured. The uninsured rate in California fell from 17.2% in 2013 to a historic low of 8.6% in 2015.In California, the uninsured rate dropped across all racial/ethnic groups, with the greatest gains seen among Latinos. Between 2013 and 2015, 1.5 million additional Latinos gained coverage, and the uninsured rate in this population fell from 23% to 12%.The sources of coverage for the more than 5 million Californians insured under the ACA include:1.4 million bought insurance on Covered California.3.7 million Californians enrolled in Medi-Cal under the ACA expansion -- representing more than a quarter of the 13.6 million Californians now covered under Medi-Cal.Hundreds of thousands of young adults under the age of 26 were able to stay on their parents' plan.Over 1 million Californians receive financial assistance to afford coverage through Covered California:Of Covered California's 1.4 million consumers, 1.2 million receive federal subsidies to help them pay their monthly premiums.The average federal premium subsidy per household is $438/month.The California Health Care Foundation will work to protect the coverage and access to care that millions of Californians have gained under the ACA. We remain committed to building a health care system that works for all Californians.
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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".