Factors Associated With Study Attrition Among HIV-Infected Risky Drinkers in St. Petersburg, Russia
Bibliographic record
Abstract
BACKGROUND: Participant attrition in HIV longitudinal studies may introduce bias and diminish research quality. The identification of participant characteristics that are predictive of attrition might inform retention strategies. OBJECTIVE: The study aimed to identify factors associated with attrition among HIV-infected Russian risky drinkers from the secondary HIV prevention HERMITAGE trial. We examined whether current injection drug use (IDU), binge drinking, depressive symptoms, HIV status nondisclosure, stigma, and lifetime history of incarceration were predictors of study attrition. We also explored effect modification due to gender. METHODS: Complete loss to follow-up (LTFU), defined as no follow-up visits after baseline, was the primary outcome, and time to first missed visit was the secondary outcome. We used multiple logistic regression models for the primary analysis, and Cox proportional hazards models for the secondary analysis. RESULTS: Of 660 participants, 101 (15.3%) did not return after baseline. No significant associations between independent variables and complete LTFU were observed. Current IDU and HIV status nondisclosure were significantly associated with time to first missed visit (adjusted hazard ratio [AHR], 1.39; 95% CI, 1.03-1.87; AHR, 1.38; 95% CI, 1.03-1.86, respectively). Gender stratified analyses suggested a larger impact of binge drinking among men and history of incarceration among women with time to first missed visit. CONCLUSIONS: Although no factors were significantly associated with complete LTFU, current IDU and HIV status nondisclosure were significantly associated with time to first missed visit in HIV-infected Russian risky drinkers. An understanding of these predictors may inform retention efforts in longitudinal studies.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".