Exploring the link between sociodemographic factors and barriers to adherence: survey data collected from people with HIV
Bibliographic record
Abstract
Context At-risk subpopulations of people with HIV (PWH) in Montreal may experience sub-optimal adherence to antiretroviral therapy (ART) due to barriers. However, what barriers these subpopulations disproportionately face remains unclear. Objective This study aims to establish a risk stratification model to determine which sociodemographic groups are most affected by barriers. Study design and analysis A cross-sectional survey between January and November 2022 was self-administered online and in-person. Setting or Dataset PWH were recruited using convenience sampling at the Chronic Viral Illness Service (CVIS), McGill University Health Centre and HIV community centres (REZO, AIDS Community Care Montreal) in Montreal, Canada. Population People with HIV. Intervention/instrument The survey examined 5 sociodemographic variables (gender, age, sexual orientation, immigration status, and education level) and ART adherence barriers within six domains, identified from prior literature. Barrier domains included thoughts and feelings (e.g., about HIV, ART); activities; social and material context; medication; health experience; and healthcare services. Barrier items were measured on a 0 (no difficulty) - 10 (maximum difficulty with adherence in the last 4 weeks) visual analogue scale, which were categorized as 0, 1-3, and 4-10. Outcome Measures Hierarchical clustering was employed to identify sociodemographic groups and survey response patterns. The number of clusters were determined by a distribution table and within-sum-of-square plots. A descriptive analysis was performed on sociodemographic features and labels were assigned based on those features. Results Data from 221 PWH were analyzed. Mean age was 51.1 years (SD=12.5). Two-thirds were men (n=148;67%). Five clusters were determined. Three clusters had primarily reported difficulty with adherence (i.e. adherence barriers) (1-3/10: %, 4-10/10: %), labelled as 1) male immigrants with a post-secondary education (21%, 68%); 2) heterosexual immigrants with a secondary education (70%, 16%); and 3) heterosexual female immigrants (13%, 31%). Two clusters primarily reported no adherence difficulties (0/10: %): 4) homosexual males (84%); and 5) homosexual males with post-secondary education (48%). Conclusions This analysis reflects the potential role of recognizing PWH sociodemographic profiles in HIV care, such as immigration status, to identify and manage barriers to 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".