Conceptualizing a sexual health information serious game for post-secondary students in British Columbia
Bibliographic record
Abstract
Serious games is a field that uses gamification and game design to engage users in learning and other non-entertainment purposes. This study aimed to conceptualize and test the potential of a serious game resource that would disseminate sexual health information to post-secondary students aged 18-29. The study comprised the development of a survey based on a review of the literature and the operationalization of game dimensions that could be used in the resource. The survey was distributed through convenience and snowball sampling. A total of 114 responses were included in the survey dataset. Findings indicated that sexual health education at the secondary school level varied greatly and was inconsistently delivered, with an emphasis on negative framing of prevention topics such as STIs, contraception, and abstinence. Sexual health topics both encountered and wanted in the post-secondary setting were discerned by respondents, with gaps identified between these which the proposed resource may address. Respondents’ post-secondary information seeking behaviours focused on digital methods and a desire for connection with medical experts and social resources. Based on respondents’ preferences for various game mechanics, mobile platforms, and interest in using a serious game to explore sexual health, several next steps for the design of a serious game resource are proposed - including critical areas of knowledge for the resource to address and further research needed to inform future design choices.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".