Bibliographic record
Abstract
An estimated 5.5 million employees are now enrolled in high-deductible health plans (HDHPs) with a tax savings option, such as a health savings account (HSA) or a health reimbursement arrangement (HRA). According to the Kaiser Family Foundation’s 2008 Annual Survey of Employer Health Benefits, the growth in HDHPs has increased mainly among companies that employ between 3 and 199 workers, with 13 percent of employees in these small companies now enrolled in a HDHP, up from 8 percent in 2007. For companies with more than 200 employees, the 5 percent inclusion rate is about the same as the previous year. Premiums are typically lower than for other plans, but the deductibles are high. On average, the annual deductible for single coverage is $2,010 for HSAs and $1,552 for HRAs. HDHPs generally have not caught on among patients who need biologics, who would be likely to meet their deductibles quickly. Most companies offering HDHPs reported that cost savings were, to them, the most successful outcome of these plans. The greatest challenge in implementing a HDHP was educating employees about the difference between these and traditional managed care plans. More than a quarter of employers offering HSAs contribute nothing to their employees’ health savings accounts.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".