Gamifying Sexual Education for Adolescents in a Low-Tech Setting: Quasi-Experimental Design Study
Bibliographic record
Abstract
Background Sexual education has become increasingly important as unhealthy sexual practices and subsequent health risks become more prevalent during adolescence. Traditional sex education teaching methodologies are limiting for digital natives exposed to various digital technologies. Harnessing the power of technology applications attractive to the younger generation may be a useful approach for teaching sex education. Objective The aim of this study was to improve sexual health knowledge and understanding of the problems associated with unhealthy sexual practices and address sexual and reproductive health challenges experienced in a low-tech setting. Methods A participatory design approach was used to develop the digital gamified methodology. A sample of 120 secondary school students aged 11-15 were randomly assigned to either experimental or control group for each of the 3 teaching approaches: (1) gamified instruction (actual serious games [SG] in teaching); (2) gamification (GM; making nongames, such as game-like learning); and (3) traditional teaching (TT) methods. Results The SG and GM approaches were more effective than TT methods in teaching sexual health education. Specifically, the average scores across groups demonstrated an increase of mean scores from the pre- to posttest (25.10 [SD 5.50] versus 75.86 [SD 13.16]; t119=41.252; P<.001 [2 tailed]). Analysis of variance indicated no significant differences across groups for pretest scores (F2,117=1.048, P=.35). Significant differences across groups were evident in the posttest scores. Students in the SG and GM groups had higher average scores than the TT group (F2,117=83.98; P<.001). Students reported increased learning motivation, attitude, know-how, and participation in learning (P<.001) when using SG and GM approaches. Conclusions Digital health technologies (particularly teaching and learning through gamified instruction and other novel approaches) may improve sexual health education. These findings may also be applied by practitioners in health care settings and by researchers wishing to further the development of sex education.
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 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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".