MétaCan
Menu
Back to cohort
Record W2942556531 · doi:10.1007/s10461-019-02516-6

Alone But Supported: A Qualitative Study of an HIV Self-testing App in an Observational Cohort Study in South Africa

2019· article· en· W2942556531 on OpenAlexafffund
Ricky Janssen, Nora Engel, Aliasgar Esmail, Suzette Oelofse, Anja Krumeich, Keertan Dheda, Nitika Pant Pai

Bibliographic record

VenueAIDS and Behavior · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMcGill University Health Centre
FundersDepartment of Science and Technology, Government of West BengalGrand Challenges CanadaMedical Research CouncilSouth African Medical Research CouncilUniversity of Cape TownCanadian Institutes of Health ResearchMcGill University Health Centre
KeywordsObservational studyHealth psychologyFocus groupQualitative researchTest (biology)UsabilityPhoneFlexibility (engineering)CohortPublic healthPsychologyMobile phoneMedicineApplied psychologyNursingComputer scienceBusinessSociologyMarketing

Abstract

fetched live from OpenAlex

HIV self-testing has the potential to improve test access and uptake, but concerns remain regarding counselling and support during and after HIV self-testing. We investigated an oral HIV self-testing strategy together with a mobile phone/tablet application to see if and how it provided counselling and support, and how it might impact test access. This ethnographic study was nested within an ongoing observational cohort study in Cape Town, South Africa. Qualitative data was collected from study participants and study staff using 33 semi-structured interviews, one focus group discussion, and observation notes. The app provided information and guidance while also addressing privacy concerns. The flexibility and support provided by the strategy gave participants more control in choosing whom they included during testing. Accessibility concerns included smartphone access and usability issues for older and rural users. The adaptable access and support of this strategy could aid in expanding test access in South Africa.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.131
GPT teacher head0.420
Teacher spread0.289 · 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 designObservational
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

Citations39
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueAIDS and BehaviorSame topicHIV/AIDS Research and InterventionsFrench-language works237,207