Chatbots for Sexual Health Improvement: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it