Low ACA Knowledge And Health Literacy Hinder Young Adult Marketplace Enrollment
Bibliographic record
Abstract
Since October, the focus within the media and the health policy community has been on the troubled roll-out of Healthcare.gov and some of the state websites set up to enroll people in coverage under the Affordable Care Act (ACA). But most Americans have paid little attention to how the changes taking place can affect their health insurance coverage. Despite the media frenzy, findings from the Health Reform Monitoring Survey show that only about a third of adults have heard some or a lot about the Marketplaces, and only a quarter have heard about the Medicaid expansion to low-income adults. Even for the more well-known ACA provisions, such as the expansion of dependent coverage to 26 year olds, the elimination of pre-existing condition exclusions, and the individual mandate, only about 50 percent report having heard much about those changes. This gap in awareness of the ACA’s coverage provisions may be as much to blame as the widely publicized IT problems in driving the low levels of Marketplace enrollment. As shown in the figure below, only 24.3 percent of young adults (age 18 to 30) in the target population for the Marketplaces—defined as adults with incomes above 138 percent of the federal poverty level who are either uninsured or who have private non-group coverage—were aware of the availability of subsidies for coverage purchased through the Marketplace, compared to 43.6 percent of adults age 50 to 64. Further, only 25.4 percent of young adults in the target population were aware of the availability of the Marketplaces themselves. Only 40.9 percent were aware of the individual mandate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".