Comparative Effectiveness: Agency for Healthcare Research and Quality's Process for Awarding Recovery Act Funds and Disseminating Results
Bibliographic record
Abstract
A letter report issued by the Government Accountability Office with an abstract that begins "AHRQ used its standard, competitive review processes and criteria to select the recipients of CER grants and contracts using Recovery Act funds. Specifically, to select the recipients of Recovery Act CER grants, AHRQ used its standard review process that includes peer review of grant applications, the development of funding recommendations by a team of senior officials within AHRQ, and final funding determination by the agencys director. As part of this process, AHRQ used its standard criteria to evaluate grant applications, as well as additional requirements that were specific to each funding opportunity. To select contractors who would receive Recovery Act funds, AHRQ used its standard contracting processes and criteria that are governed by the Federal Acquisition Regulation, which establishes uniform policies for acquisition of supplies and services by executive agencies, and the Public Health Service Act. These processes included an evaluation of all contract proposals using standard criteria adapted to the specific needs of each project. Between February 2009 and September 2010, AHRQ awarded $311 million of its $474 million in Recovery Act CER funds through 110 grants. AHRQ also awarded $161 million of its Recovery Act CER funding through 34 contracts. The contracts and grants AHRQ awarded supported both AHRQs agency-specific and HHSs departmentwide CER priority areas. In an effort to avoid unnecessary duplication of CER awards, AHRQ participated in HHS working groups, developed a CER spending plan, and queried HHS databases to check for duplicative awards."
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 | 0.613 | 0.661 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.020 | 0.030 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.043 | 0.018 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".