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Record W2335111383 · doi:10.1177/1470357211398447

Cartoon drawing as a means of accessing what students know about HIV/AIDS: an alternative method

2011· article· en· W2335111383 on OpenAlexaff
Harriet Mutonyi, Maureen Kendrick

Bibliographic record

VenueVisual Communication · 2011
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHuman sexualityHuman immunodeficiency virus (HIV)Sexuality educationPerceptionPublic healthPsychologyReproductive healthSociologySex educationMedicineGender studiesPopulationFamily medicineNursing

Abstract

fetched live from OpenAlex

Combating the spread of HIV/AIDS in Uganda has involved massive public education campaigns. One of the challenges of these campaigns has always involved the need to simultaneously respect and transcend cultural taboos around direct discussions about sexuality and sexual issues, particularly among youth. Research consistently shows that drawing, as a means of investigating what students know, has the potential to reveal students’ perceptions of given concepts and provides an alternative to predominantly language-based methods. Visual methods, however, have rarely been taken up in research on students’ sexual health and HIV/AIDS knowledge. This interpretive case study examines the use of cartoon drawing as a unique tool for understanding Ugandan secondary students’ conceptions of HIV/ AIDS, particularly concepts that are not directly discussed culturally.

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)0.005
metaresearch head score (Gemma)0.013
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 score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.004
Scholarly communication0.0040.004
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.162
GPT teacher head0.522
Teacher spread0.359 · 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

Citations35
Published2011
Admission routes1
Has abstractyes

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