Defining the Content of an Online Sexual Health Intervention: The MenSS Website
Bibliographic record
Abstract
BACKGROUND: Health promotion and risk reduction are essential components of sexual health care. However, it can be difficult to prioritize these within busy clinical services. Digital interventions may provide a new method for supporting these. OBJECTIVE: The MenSS (Men's Safer Sex) website is an interactive digital intervention developed by a multidisciplinary team, which aims to improve condom use in men who have sex with women (MSW). This paper describes the content of this intervention, and the rationale for it. METHODS: Content was informed by a literature review regarding men's barriers to condom use, workshops with experts in sexual health and technology (N=16) and interviews with men in sexual health clinics (N=20). Data from these sources were analyzed thematically, and synthesized using the Behavior Change Wheel framework. RESULTS: The MenSS intervention is a website optimized for delivery via tablet computer within a clinic waiting room setting. Key targets identified were condom use skills, beliefs about pleasure and knowledge about risk. Content was developed using behavior change techniques, and interactive website features provided feedback tailored for individual users. CONCLUSIONS: This paper provides a detailed description of an evidence-based interactive digital intervention for sexual health, including how behavior change techniques were translated into practice within the design of the MenSS website. Triangulation between a targeted literature review, expert workshops, and interviews with men ensured that a range of potential influences on condom use were captured.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".