User Views on Online Sexual Health Symptom Checker Tool: Qualitative Research
Bibliographic record
Abstract
BACKGROUND: Delayed diagnosis and treatment of sexually transmitted infections (STIs) contributes to poorer health outcomes and onward transmission to sexual partners. Access to best-practice sexual health care may be limited by barriers such as cost, distance to care providers, sexual stigma, and trust in health care providers. Online assessments of risk offer a novel means of supporting access to evidence-based sexual health information, testing, and treatment by providing more individualized sexual health information based on user inputs. OBJECTIVE: This developmental evaluation aims to find potential users' views and experiences in relation to an online assessment of risk, called iSpySTI (Melbourne Sexual Health Center), including the likely impacts of use. METHODS: Individuals presenting with urogenital symptoms to a specialist sexual health clinic were given the opportunity to trial a web-based, Bayesian-powered tool that provides a list of 2 to 4 potential causes of their symptoms based on inputs of known STI risk factors and symptoms. Those who tried the tool were invited to participate in a once-off, semistructured research interview. Descriptive, action, and emotion coding informed the comparative analysis of individual cases. RESULTS: Findings from interviews with 14 people who had used the iSpySTI tool support the superiority of the online assessment of STI risk compared to existing sources of sexual health information (eg, internet search engines) in providing trusted and probabilistic information to users. Additionally, potential users reported benefits to their emotional well-being in the intervening period between noticing symptoms and being able to access care. Differences in current and imagined urgency of health care seeking and emotional impacts were found based on clinical diagnosis (eg, non-STI, curable and incurable but treatable STIs) and whether participants were born in Australia or elsewhere. CONCLUSIONS: Online assessments of risk provide users experiencing urogenital symptoms with more individualized and evidence-based health information that can improve their health care-seeking and provide reassurance in the period before they can access care.
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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 | 0.035 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".