Persuasive Application for Sex Education
Bibliographic record
Abstract
Young adults are curious, and due to a lack of proper knowledge of sexual health, they often tend to make risky sexual choices. Thus, sex education is important, as it increases individuals' knowledge of sexual health, teaches about safe sex practices and healthy sexual behavior, and enables one to make informed decisions. However, in some countries, like India, sex education is highly neglected due to the societal stigma around the topic of sex. Persuasive interventions have shown success in increasing awareness and promoting desired behaviour. However, most of these interventions consider developed countries. This work aims at investigating persuasive intervention for sex education specifically catered to the Indian audience. Particularly, this work discusses using a persuasive mobile app to help individuals learn about sexual health. As a first step to achieving our goal, we designed a prototype for an app called ‘Sex-Educated’ with persuasive features that aim to promote safe sexual practices and increase awareness about risky sexual behaviour among Indian adults. To evaluate the effectiveness of our app with persuasive design and features, we conducted a user study with 57 participants. Our results show that Indian adults are motivated to use a gamified persuasive app for sexual health education. Our app with persuasive features is much more effective to encourage learning, create awareness, and bring about positive attitude and behavioral changes towards sexual health.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".