Improving access to test results for participants in bio-behavioural surveys of people who inject drugs
Bibliographic record
Abstract
Bio-behavioural surveys of people who inject drugs (PWID) evolved from unlinked anonymous monitoring (UAM) of human immunodeficiency virus (HIV) incidence and prevalence, which began in some high-income countries in the late 1980s. UAM was conducted purely for surveillance purposes and test results were not returned to participants. Later, the importance of collecting data on behavioural risk factors was recognised, leading to the development of bio-behavioural surveys of PWID, which today are conducted regularly in several countries. Typically, these surveys recruit participants from venues providing harm reduction services and involve behavioural questionnaires and dried blood spot (DBS) testing for HIV and hepatitis C (HCV). DBS test results are not returned to participants; instead, countries offer varied systems of on-site testing separate from the bio-behavioural testing or provide referrals to external testing services. In this commentary, we trace the history of bio-behavioural surveys of PWID from their origins to the present day to explain how the methodologies evolved, along with the ethical considerations underlying them. We highlight the dramatic improvements in treatments for HIV and HCV over the past thirty years and the corresponding need to ensure that bio-behavioural survey participants can access low-barrier and timely testing. We review the pros and cons of different strategies for providing test results to participants and argue that the return of DBS results collected as part of bio-behavioural surveys warrants consideration as an additional tool to improve testing access for participants. Any changes should be informed by the perspectives of participants, study site personnel and investigators.
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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 | 0.036 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".