The Stopping the Spread of Human Immunodeficiency Virus/Acquired Immune Deficiency Syndrome through Relationship Engagement Study: An Opportunity for Human Immunodeficiency Virus Prevention in African American Adolescents with HIV-Positive Mothers
Bibliographic record
Abstract
In 2018 Blacks/African Americans made up 13% of the female population but accounted for 58% of diagnoses of human immunodeficiency virus (HIV) infection among females.1 Studies show that women have always been underrepresented in HIV/acquired immune deficiency syndrome (AIDS) studies, however, the coronavirus disease-2019 (COVID-19) pandemic has further exacerbated the existing barriers in HIV research.2 Additionally, with social distancing guidelines in place due to COVID-19, research that requires partnership development with gatekeepers and community-based organizations may not effectively transition to virtual or other remote settings.2 Black women’s underrepresentation in HIV research is in part due to inadequate recruitment strategies.3 While we use technology to try to compensate for the lack of human connection in research due to the COVID-19 pandemic, it has made recruitment more difficult.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".