Barriers and Facilitators Affecting the HIV Care Cascade for Migrant People Living with HIV in Organization for Economic Co-Operation and Development Countries: A Systematic Mixed Studies Review
Bibliographic record
Abstract
Migrants in countries affiliated with the Organization for Economic Co-operation and Development (OECD) have a higher risk of acquiring HIV, experience delayed HIV diagnosis, and have variable levels of engagement with HIV care and treatment when compared to native-born populations. A systematic mixed studies review was conducted to generate a multilevel understanding of the barriers and facilitators affecting HIV Care Cascade steps for migrant people living with HIV (MLWH) in OECD countries. Medline, Embase, Scopus, CINAHL, and the Cochrane Library were searched on March 25, 2020. Screening, critical appraisal, and analysis were conducted independently by two authors. We used qualitative content analysis and the five-level Socio-Ecological Model (i.e., individual, interpersonal, organizational, community, and policy) to categorize barriers and facilitators. Fifty-nine studies from 17 OECD countries were included. MLWH faced similar barriers and facilitators regardless of their host country, ethnic and geographic origins, or legal status. Most barriers and facilitators were associated with the individual and organizational levels and centered around retention in HIV care and treatment. Adapting clinical environments to better address MLWH's competing needs via multidisciplinary models would address retention issues across OECD countries.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".