Advancing Migrant Access to Health Services in Europe (AMASE): Protocol for a Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Migrants form a substantial proportion of the population affected by the human immunodeficiency virus (HIV) epidemic in Europe, yet HIV prevention for this population is hindered by poor understanding of access to care and of postmigration transmission dynamics. OBJECTIVE: We present the design and methods of the advancing Migrant Access to health Services in Europe (aMASE) study, the first European cross-cultural study focused on multiple migrant populations. It aims to identify the structural, cultural, and financial barriers to HIV prevention, diagnosis, and treatment and to determine the likely country of HIV acquisition in HIV-positive migrant populations. METHODS: We delivered 2 cross-sectional electronic surveys across 10 countries (Belgium, France, Germany, Greece, Italy, the Netherlands, Portugal, Spain, Switzerland, and United Kingdom). A clinic survey aimed to recruit up to 2000 HIV-positive patients from 57 HIV clinics in 9 countries. A unique study number linked anonymized questionnaire data to clinical records data (viral loads, CD4 cell counts, viral clades, etc). This questionnaire was developed by expert panel consensus and cognitively tested, and a pilot study was carried out in 2 countries. A Web-based community survey (n=1000) reached those living with HIV but not currently accessing HIV clinics, as well as HIV-negative migrants. It was developed in close collaboration with a community advisory group (CAG) made up of representatives from community organizations in 9 of the participating countries. The CAG played a key role in data collection by promoting the survey to higher-risk migrant groups (sub-Saharan Africans, Latin Americans, men who have sex with men, and people who inject drugs). The questionnaires have considerable content overlap, allowing for comparison. Questions cover ethnicity, migration, immigration status, HIV testing and treatment, health-seeking behavior, sexual risk, and drug use. The electronic questionnaires, which were available in 15 languages, allowed for complex routing, preventing respondents from answering irrelevant questions. RESULTS: In total, we recruited 2249 participants from 57 HIV clinics as part of the clinic survey and retrieved 1637 complete responses as part of the community survey. CONCLUSIONS: The findings will provide much-needed information for improving HIV prevention interventions and access to services for migrant communities.
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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 | 0.037 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.056 | 0.014 |
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".