Study of the Impact of the ACA Implementation in Kentucky - Quarterly Snapshot: April-June 2015
Bibliographic record
Abstract
The Foundation for a Healthy Kentucky has contracted with State Health Access Data Assistance Center (SHADAC), a health policy research institute at the University of Minnesota, to study how the Affordable Care Act (ACA) is impacting Kentuckians. SHADAC released its second quarterly health data snapshot which covers the April-June, 2015 timeframe. Highlights of this latest health data snapshot include:From December 2013 to June 2015, Kentucky's uninsurance rate dropped from 20.4% to 9.0% - a steeper decline than that of neighboring states and the nation as a whole.Medicaid funded thousands of preventive services during the quarter, including more than 10,000 breast cancer screenings. Over 9,000 of these breast cancer screenings were among Medicaid expansion enrollees, and nearly 1,200 were among traditional Medicaid enrollees.Children obtained the majority of Medicaid's dental visits, representing 66% of the more than 250,000 dental visits provided during the quarter (among Kentuckians ages 0-64).The proportion of marketplace (kynect) enrollees receiving premium assistance in the form of advance premium tax credits was lower than the national proportion (approx. 70% in Kentucky compared to around 84% for the nation).Kentucky's 11.4 percentage point drop in the rate of uninsured residents continued to outpace neighboring states (Illinois, Indiana, Missouri, Ohio, Tennessee, Virginia and West Virginia) which averaged a 5.2 percentage point drop. The national decline in uninsured in the same timeframe was 5.7 percentage points.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".