A Digital Pornography Education Prototype Co-Designed With Young People: Formative Evaluation
Bibliographic record
Abstract
Background: Interventions to help young people make sense of sex and relationships in the context of widely available pornography are becoming increasingly supported in school settings. However, young people who experience disruptions to their education often have less access to such programs. Digital platforms may offer a more accessible method to deliver tailored sexual health and pornography literacy to young people who are disengaged from mainstream schooling, or who experience other types of structural disadvantage. Objective: This study aimed to describe the formative evaluation of "The Gist" a co-designed online sexual health education and pornography literacy prototype designed to meet the sexual health information needs of structurally marginalized young people in Australia. Methods: We conducted iterative workshops with 33 young people aged between 15 and 24 years recruited from an alternative education school in Melbourne, Australia. Through interactive activities, participants evaluated the overall prototype design, including its usability, desirability, inclusiveness, and potential for impact. Results: Participants reported The Gist to be easy to use (17/20, 85%) and safe (19/23, 83%), with "hot" branding (25/30, 83%). However, perceived content relevance was dependent on the participants' existing level of sexual health knowledge and experience, with only 31% (7/23) agreeing that "The Gist feels like it was made for me." The interactive learning activities such as the debunked (myth-busting) and quiz features were among the most used and well-liked on The Gist platform. Low unprompted engagement with the prototype outside of facilitated workshop settings also confirmed previous researcher postulations that The Gist as a standalone digital platform is unlikely to meet the needs of this population group. Further design refinements are needed to improve user experience, including more interactive activities and visual information in place of heavily text-based features. Conclusions: This study provides important insights into the design and sexual health information needs of structurally marginalized young people. Further research is needed to assess the overall efficacy of The Gist prototype, as well as its ability to positively influence young people's sexual attitudes, beliefs, and behaviors. Future iterations should consider hybrid or face-to-face delivery models to better capture student engagement.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".