Implementing and retaining a large-scale technology-mediated cohort to study HIV incidence and PrEP uptake among vulnerable cisgender men as well as transgender individuals in the United States, the Together 5000 cohort
Bibliographic record
Abstract
HIV remains a significant public health concern requiring innovative solutions. Widespread internet access and advancing health technologies (e.g., home-based HIV/STI testing) have often made research and intervention implementation more efficient and effective. The purpose of this analysis is to describe a technology-mediated HIV prevention cohort's implementation, participant engagement strategies, and methods to overcome study retention challenges. Participants residing in the United States or territories were recruited from geospatial social networking applications between October 2017 and June 2018. Enrolled participants completed annual online surveys and at-home HIV testing-baseline, 12-, 24-, 36-, and 48-month follow-up. Multiple adjusted logistic regression models were used to determine sociodemographic and behavioral characteristics associated with completing each survey and returning a HIV specimen collection kit. Study response probability weights were calculated to account for attrition. Results suggest several sociodemographic and behavioral characteristics were associated with completing study activities. Importantly, participants who were Black had lower odds of completing surveys and HIV testing. This cohort demonstrated feasibility for recruiting and retaining a cohort of 5000 HIV-vulnerable individuals and identified 569 HIV infections. Our findings highlight many benefits of conducting internet-mediated studies; however, these studies face unique challenges that may require post-hoc analytic solutions or effective retention strategies.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| 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".