Differential uptake and effects of digital sexually transmitted and bloodborne infection testing interventions among equity-seeking groups: a scoping review
Bibliographic record
Abstract
BACKGROUND: Digital sexually transmitted and bloodborne infection (STBBI) testing interventions have gained popularity. However, evidence of their health equity effects remains sparse. We conducted a review of the health equity effects of these interventions on uptake of STBBI testing and explored design and implementation factors contributing to reported effects. METHODS: (2010). We searched OVID Medline, Embase, CINAHL, Scopus, Web of Science, Google Scholar and health agency websites for peer-reviewed articles and grey literature comparing uptake of digital STBBI testing with in-person models and/or comparing uptake of digital STBBI testing among sociodemographic strata, published in English between 2010 and 2022. We extracted data using the Place of residence, Race, Occupation, Gender/Sex, Religion, Education, Socioeconomic status (SES), Social capital and other disadvantaged characteristics (PROGRESS-Plus) framework, reporting differences in uptake of digital STBBI testing by these characteristics. RESULTS: We included 27 articles from 7914 titles and abstracts. Among these, 20 of 27 (74.1%) were observational studies, 23 of 27 (85.2%) described web-based interventions and 18 of 27 (66.7%) involved postal-based self-sample collection. Only three articles compared uptake of digital STBBI testing with in-person models stratified by PROGRESS-Plus factors. While most studies demonstrated increased uptake of digital STBBI testing across sociodemographic strata, uptake was higher among women, white people with higher SES, urban residents and heterosexual people. Co-design, representative user recruitment, and emphasis on privacy and security were highlighted as factors contributing to health equity in these interventions. CONCLUSION: Evidence of health equity effects of digital STBBI testing remains limited. While digital STBBI testing interventions increase testing across sociodemographic strata, increases are lower among historically disadvantaged populations with higher prevalence of STBBIs. Findings challenge assumptions about the inherent equity of digital STBBI testing interventions, emphasising the need to prioritise health equity in their design and evaluation.
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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 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".