Returned Peace Corps Volunteers: Labor and Peace Corps Need Joint Approach to Monitor Access to and Quality of Health Care Benefits
Bibliographic record
Abstract
A letter report issued by the Government Accountability Office with an abstract that begins "From 2009 through 2011, the Department of Labor (DOL) provided a total of about $36 million in Federal Employees' Compensation Act (FECA) benefits--health and other benefits--for Peace Corps volunteers who have returned from service abroad (volunteers). Specifically, DOL provided about $22 million in health care benefits for these volunteers in the form of reimbursements for medical expenses related to service-connected injuries and illnesses, and $13.8 million in other benefits, such as reimbursement for travel expenses incurred when seeking medical care. During this period, approximately 1,400 volunteers each year received these health care benefits under the FECA program. The most common types of medical conditions for which DOL provided reimbursements were mental, emotional, and nervous conditions; dental; other/nonclassified diseases; and infectious or parasitic diseases. These four medical conditions accounted for more than a quarter of all medical reimbursements for volunteers under FECA from 2009 through 2011."
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".