“Mini Dial-A-Nurses” and “Good Brands“: What Are the Desirable Features of Online HIV/STI Risk Calculators?
Bibliographic record
Abstract
A wide variety of risk calculators estimate individuals' risk for HIV/sexually transmitted infections (STI) online. These tools can help target HIV/STI screening and optimize clinical decision-making. Yet, little evidence exists on suitable features for these tools to be acceptable to end-users. We investigated the desirable characteristics of risk calculators among STI clinic clients and testing service providers. Participants interacted with online HIV/STI risk calculators featuring varied target audiences, completion lengths, and message outputs. Thematic analysis of focus groups identified six qualities that would make risk calculators more appealing for online client use: providing personalized risk assessments based on users' specific sexual behaviors and HIV/STI-related concerns; incorporating nuanced risk assessment and tailored educational information; supplying quantifiable risk estimates; using non-stigmatizing and inclusive framing; including explanations and next steps; and developing effective and appropriate branding. Incorporating these features in the design of online HIV/STI risk calculators may improve their acceptability among end-users.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".