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Record W2952561988 · doi:10.18192/ejre.v6i1.2064

Discoursing Health Literacies for HIV/AIDS Education

2018· article· en· W2952561988 on OpenAlexaffvenue
Hembadoon Iyortyer Oguanobi

Bibliographic record

VenueEducation Journal - Revue de l éducation · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHIV/AIDS Impact and Responses
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsStigma (botany)LiteracyCurriculumHuman immunodeficiency virus (HIV)Health literacySpace (punctuation)Health educationSociologyReading (process)MedicinePedagogyEconomic growthPolitical sciencePublic healthHealth careFamily medicineNursingComputer science

Abstract

fetched live from OpenAlex

Health literacy is an important tool for HIV/AIDS education. It provides a space for students to use local literacy tools such as films, literature, and arts to explore ways of managing the HIV/AIDS virus in communities ravaged by the disease. HIV/AIDS affects the lives of millions of people in many African countries and requires a robust strategy by educators to tackle the epidemic and create safe spaces for students in schools and communities where young people face stigma and discrimination for having the virus, or living with people who have the virus. In this paper, the author discusses how students in some African countries respond to locally manufactured HIV literacy educational tools produced by members of the community. The author makes the case that it is important for schools to incorporate locally manufactured HIV/AIDS health literacy instruction into the curriculum; this would allow young people to engage with health literacies that are resonant of their embodied experiences. Keywords: Africa, discrimination, health literacies, HIV/AIDS, school programs

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)0.004
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.004
Scholarly communication0.0050.002
Open science0.0010.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0160.002

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 head0.075
GPT teacher head0.362
Teacher spread0.287 · 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
Published2018
Admission routes2
Has abstractyes

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Same venueEducation Journal - Revue de l éducationSame topicHIV/AIDS Impact and ResponsesFrench-language works237,207