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Record W2788031063 · doi:10.5539/gjhs.v10n3p172

Adolescent School-Based Sexual Health Education and Training: A Literature Review on Teaching and Learning Strategies

2018· review· en· W2788031063 on OpenAlexvenueno aff
Hussein Haruna, Xiao Hu, Samuel Kai Wah Chu

Bibliographic record

VenueGlobal Journal of Health Science · 2018
Typereview
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
Fundersnot available
KeywordsAttractivenessPsychological interventionReproductive healthPsychologyAppealHabitMedical educationTeaching methodPedagogyMedicineSocial psychologyPopulationEnvironmental health

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.094
GPT teacher head0.483
Teacher spread0.389 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations25
Published2018
Admission routes1
Has abstractyes

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