Eliciting Preferences for HIV Prevention Technologies: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Many human immunodeficiency virus (HIV) prevention technologies (pre-exposure prophylaxis, microbicides, vaccines) are available or in development. Preference elicitation methods provide insight into client preferences that may be used to optimize products and services. Given increased utilization of such methods in HIV prevention, this article identifies and reviews these methods and synthesizes their application to HIV prevention technologies. METHODS: In May 2020, we systematically searched peer-reviewed literature in PubMed, CINAHL, and Web of Science for studies employing quantitative preference elicitation methods to measure preferences for HIV prevention technologies among populations of any age, sex, or location. Quality assessment used an existing checklist (PREFS) and a novel adaptation of the Newcastle-Ottawa Scale (PROSPERO #CRD42018087027). RESULTS: We screened 5022 titles and abstracts, reviewed 318 full texts, and included 84 studies. Common methods employed were discrete-choice experiment (33%), conjoint analysis (25%), and willingness-to-participate/try/accept (21%). Studies were conducted in 25 countries and had a mean of 768 participants (range = 26-7176), two-thirds of them male. Common HIV prevention technologies included pre-exposure prophylaxis (23%), voluntary testing and counseling (19%), HIV self-testing (17%), vaccines (15%), and topical microbicides (9%). Most attributes focused on product design (side effects, frequency), service design (provider type, location), acceptability or willingness to accept/pay; results are summarized in these categories, by prevention type. Mean quality-adapted Newcastle-Ottawa Scale score was 4.5/8 (standard deviation = 2.1) and mean PREFS scores was 3.47/5 (standard deviation = 0.81). CONCLUSIONS: This review synthesizes extant literature on quantitative measurement of preferences for HIV prevention technologies. This can enable practitioners to improve prevention products and interventions, and ultimately reduce HIV incidence.
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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 | 0.022 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".