A qualitative investigation of HIV treatment dispensing models and impacts on adherence among people living with HIV who use drugs
Bibliographic record
Abstract
Antiretroviral therapy (ART) dispensing is strongly associated with treatment adherence. Among illicit drug-using populations, whom experience greater structural barriers to adherence, directly administered antiretroviral therapy (DAAT) is often regarded as a stronger predictor of optimal adherence over self-administered medications. In Vancouver, Canada, people living with HIV (PLHIV) who use drugs and live in low-income housing are a critical population for treatment support. This group is typically able to access two key DAAT models, daily delivery and daily pickup, in addition to ART self-administration. This ethno-epidemiological qualitative study explores how key dispensing models impact ART adherence among PLHIV who use drugs living in low-income housing, and how this is framed by structural vulnerability. Semi-structured interviews lasting 30-45 minutes were conducted between February and May 2018 with 31 PLHIV who use drugs recruited from an ongoing prospective cohort of PLHIV who use drugs. Interviews were audio-recorded, transcribed verbatim, and analyzed using QSR International's NVivo 12 software. Interviews focused on housing, drug use, and HIV management. Models that constrained agency were found to have negative impacts on adherence and quality of life. Treatment interruptions were framed by structural vulnerabilities (e.g., housing vulnerability) that impacted ability to maintain adherence under certain dispensing models, and led participants to consider other models. Participants using DAAT models which accounted for their structural vulnerabilities (e.g., mobility issues, housing instability), credited these models for their treatment adherence, but also acknowledged factors that constrained agency, and the negative impacts this could have on both adherence, and quality of life. Being able to integrate ART into an established routine is key to supporting ART adherence. ART models that account for the structural vulnerability of PLHIV who use drugs and live in low-income housing are necessary and housing-based supports could be critical, but the impacts of such models on agency must be considered to ensure optimal adherence.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".