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Record W2165625312 · doi:10.19030/jier.v11i1.9097

Students Talk About Their HIV/AIDS Education Courses: A Case Of Addis Ababa, Ethiopia

2015· article· en· W2165625312 on OpenAlexaff
Mariam Mekdelawit Sambe

Bibliographic record

VenueJournal of International Education Research (JIER) · 2015
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsConcordia University
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)PsychologyMathematics educationMedical educationSociologyPedagogyMedicineFamily medicine

Abstract

fetched live from OpenAlex

The objective of this research was to explore how Ethiopian high school students experienced the HIV/AIDS education programs offered in their schools. The project also examined gender differences in the way HIV/AIDS education was perceived and the implications for the instructional design of the programs. A total of 15 high school students (eight females and seven males between the ages of 13 and 18) in Addis Ababa, the capital of Ethiopia, were recruited through purposeful sampling. Data were collected using two focus groups with each group comprising one gender. This separation of genders was ideal to enable participants to express their thoughts freely and to observe possible gender divergences. Findings: All participants agreed that school-based HIV/AIDS education was essential but that unfortunately, most of the programs that students attended were neither interesting nor beneficial. Gender differences were observed with females asking for in-school AIDS education to completely modify its focus and males feeling that the education’s current strategies should simply be improved. The implications of these results are discussed along with recommendations for further research.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0250.005
Scholarly communication0.0060.002
Open science0.0020.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.245
GPT teacher head0.602
Teacher spread0.357 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations0
Published2015
Admission routes1
Has abstractyes

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