Plasma HIV-1 RNA viral load rebound among people who inject drugs receiving antiretroviral therapy (ART) in a Canadian setting: an ethno-epidemiological study
Bibliographic record
Abstract
BACKGROUND: People who inject drugs (PWID) living with HIV often experience sub-optimal antiretroviral therapy (ART) treatment outcomes, including HIV plasma viral load (PVL) rebound. While previous studies have identified risk factors for PVL rebound among PWID, no study has examined the perspectives of PWID who have experienced PVL rebound episodes. We conducted an ethno-epidemiological study to investigate the circumstances surrounding the emergence of rebound episodes among PWID in Vancouver, BC, Canada. METHODS: Comprehensive clinical records linked to a community-based prospective observational cohort of HIV-positive drug users were used to identify PWID who had recently experienced viral rebound. In-depth qualitative interviews with 16 male and 11 female participants explored participant perspectives regarding the emergence of viral rebound. A timeline depicting each participant's HIV viral load and adherence to ART was used to elicit discussion of circumstances surrounding viral rebound. FINDINGS: Viral rebound episodes were shaped by interplay between various individual, social, and environmental factors that disrupted routines facilitating adherence. Structural-environmental influences resulting in non-adherence included housing transitions, changes in drug use patterns and intense drug scene involvement, and inadequate care for co-morbid health conditions. Social-environmental influences on ART adherence included poor interactions between care providers and patients producing non-adherence, and understandings of HIV treatment that fostered intentional treatment discontinuation. CONCLUSIONS: This study describes key pathways which led to rebound episodes among PWID receiving ART and illustrates how environmental forces may increase vulnerability for non-adherence leading to treatment failure. Our findings have potential to help inform interventions and supports that address social-structural forces that foster non-adherence among PWID.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".