Design, Develop, and Evaluate a Collaborative Serious Game to Enhance 18-24-year-olds' Sexual Communication and Negotiation Skills on Safer Sex and Condom Use
Bibliographic record
Abstract
The aims of this study are threefold. First, the study aims to understand the main reasons that stop 18-24-year-olds from communicating condom use and safer sex. Based on findings supported by empirical studies in the literature and interviews with sexual health researchers in Canada, this study describes how a collaborative serious game integrates the principles of serious games with practices of safer sexual communication and negotiation. Finally, it includes an analysis of how 18- to 24-year-olds report practicing safer sexual communication and negotiation skills through participation in the collaborative serious game and what insights (a) 18-24-year-old participants and (b) sexual health experts share about the game that can inform future design iterations of this game. Forty participants aged 18-24 played the game and reported enhanced communication and language skills, raised awareness and reduced stigma around safer sex communication and condom use. The potential of the game in enhancing the participants' language skills (i.e., learning the language such as words, phrases, expressions) of communication and negotiation showed the highest frequency. Language skills and communications skills together comprised 28.5% of the overall feedback. The second most frequent theme was about the efficiency of the game in normalizing conversations around sex and condom use and removing the awkwardness around such topics. The game seemed to allow participants to practice dialogue and scenarios that extend beyond what they experienced in formal sex education in school. Participants also provided a range of recommendations for the next iteration of the game. To design the serious game, I followed a process of Design-Based Research (DBR) (Anderson & Shattuck, 2012) model and followed the four phases of DBR proposed by Reeves (2006). The study's findings aid other researchers in the field and offer insights to enhance sexual health education. With the increasing STIs in Canada, COVID-19's impact, and young people's reliance on online resources for answers, this research is timely. Moreover, the study contributes to the scarce research on collaborative serious games to improve 18-24-year-olds' sexual communication and negotiation skills. Limitations and implications of the design and of the game, as experienced by participants are discussed.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".