Adolescent School-Based Sexual Health Education and Training: A Literature Review on Teaching and Learning Strategies
Bibliographic record
Abstract
OBJECTIVES: The objective of this review is to gain an understanding of the teaching approaches used and their effectiveness in imparting sexual health literacy amongst school adolescents. The intention is to design interventions for effective sexual health education in our study setting.METHODS: We reviewed various literature related to adolescent sexual health education studies that have been conducted by prior researchers. We also provide an overview of the teaching and learning methods used.RESULTS: Through this literature review, we learned that GBL and gamification were carried out primarily in developed countries, outside of Africa. It has been observed that both GBL and Gamification are effective and efficient in the transformation of knowledge, as they influence students’ learning processes through engagement, enjoyment, excitement, attractiveness, and participation. They also foster critical thinking skills, improve confidence, increase motivation, and stimulate a habit of self-regulatory learning among students.CONCLUSIONS: Ensuring the impartation of sexual health knowledge can also be achieved by designing and applying effective innovation teaching methods that appeal to today’s youth, such as GBL and Gamification. We will design GBL and Gamification methods and evaluate their effectiveness amongst Africans students, specifically among Tanzanian school adolescents.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".