Excellence and Equality in Health Care
Bibliographic record
Abstract
As the United States steps up to the historic opportunity offered by the Affordable Care Act, the imperative for health care transformation to meet the needs of an increasingly diverse population is indisputable. The sheer increase of uninsured Americans signing up for coverage highlights this point. It is estimated that more than 20 million people have signed up for insurance coverage under the new law; and recently, the proportion of adults lacking coverage has fallen by 26% since the third quarter of 2014 and May 2014.1 Many of these new enrollees are likely younger, low-income, and members of racial and ethnic minority groups because these groups are more likely to be without coverage. This is reflective of uninsured veterans who stand to benefit by the new law,2 because these groups are reflective of the growing veteran population that will likely seek out services in the Veterans Health Administration (VHA). In fact, the VHA is preparing for anticipated increases in veterans from diverse groups in the coming decades. During the rollout of the Affordable Care Act, the Department of Veterans Affairs (VA) identified 2.2 million veterans who were likely to be uninsured and eligible to enroll with the VA. As of April 2014, more than 20 400 veterans enrolled in response to the initial VA outreach efforts.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.018 | 0.032 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".