Retrospective Analysis of HIV Pre-exposure Prophylaxis (PrEP) Awards Under Executive Order 14168 on Gender Ideology in the U.S. 2012-2025
Bibliographic record
Abstract
Objectives: To assess the potential impact of Executive Order (EO) 14168, which restricts the use of terms such as "gender," "transgender," and "LGBT," on HIV PrEP-specific research funding, communications, and publications in the United States. Study design: A retrospective analysis of federal grant awards related to HIV pre-exposure prophylaxis (PrEP) was conducted using public data sources. Methods: Award titles and abstracts were obtained from the Tracking Accountability in Government Grants System (TAGGS) for grants containing the terms "PrEP" or "pre-exposure prophylaxis" between 2012 and 2025. Grants were coded for the presence of EO-14168-restricted terminology. Award disbursement characteristics and funding amounts were analyzed, with data subset by presidential budgetary periods for political comparison. Results: Among 388 unique grants, 118 (30.4%) contained terminology that would be excluded under EO-14168. The most frequently represented restricted terms were "transgender" and "gender." Mental health research accounted for the largest share of excluded awards. Both Democratic and Republican states experienced reductions in HIV PrEP-specific funding under these exclusions. The total disbursements affected by restricted terminology amounted to nearly $160 million (USD). Conclusions: If applied retrospectively, EO-14168 would have reduced HIV PrEP-specific research funding by nearly $160 million (USD) from 2012 to 2025, with downstream effects of approximately $400 million (USD) in lost economic activity from the NIH. These restrictions could substantially limit HIV prevention research, particularly in populations disproportionately impacted by HIV.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".