Closing the Coverage Gaps: Reducing Health Insurance Disparities in Massachusetts
Bibliographic record
Abstract
Massachusetts has been exemplary in developing health insurance coverage policies to cover its residents. By 2019, the state's uninsurance rate was 3.0 percent, the lowest rate in the nation, representing about 204,000 uninsured residents. While the state's overall uninsured rate at a given point in time is low, more than twice as many people - 503,000, or 7.3 percent of the population - experienced a gap in coverage over the previous twelve months. And importantly, not all groups benefit equally. People who are Black or Hispanic, or who have lower incomes, experience significantly higher rates of uninsurance than the state population overall. As a result, these groups are more likely to face access barriers and financial insecurity associated with being uninsured.The purpose of this report is to begin charting a course toward closing the coverage gaps in Massachusetts, with a particular focus on creating a more racially and ethnically equitable system of coverage. The report and accompanying infographics describe the people in Massachusetts without health insurance and the barriers to coverage they face, including affordability, administrative complexity, and immigration, language, and cultural barriers. It then proposes a menu of policy options that address the specific circumstances in Massachusetts. The proposed options are meant to inform a statewide conversation about the best approaches to closing the remaining coverage gaps in Massachusetts and removing structural barriers that result in racial and ethnic disparities in health insurance coverage.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".