Chatbots for Sexual Health Improvement: A Systematic Review
Bibliographic record
Abstract
There is a rising interest in chatbots dedicated to enhancing sexual health. However, there is limited research on the effectiveness of these chatbots, and the current literature lacks sufficient exploration of gaps and patterns in this field. In this review, we provided an overview of the state-of-the-art research conducted on sexual health chatbots, with the goal of identifying prevalent trends, design patterns, and features. In addition, we investigated existing research gaps, challenges, and shortcomings in the landscape of sexual health chatbots. Further, we proposed potential enhancements and directions for future research and development to create more effective chatbots in this field. A systematic search and screening of the literature from the past decade (2013–2023), extracted from seven databases, yielded a total of 1040 studies, out of which 29 articles were included in the final review following screening. The findings suggest that chatbots are usable and effective tools in sexual health education, persuasion, and assistance that are appreciated for their confidentiality, efficiency, and 24/7 availability. However, their performance is hindered by limitations such as restricted scope of knowledge and challenges in understanding user inputs. Additionally, constraints such as text-only input/output modalities and a predominant reliance on the English language limit their accessibility and acceptability. There is also a crucial need for more research in low-income or lower-middle-income countries, where individuals require increased sexual health education and support.
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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 | 0.012 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".