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Record W2126333613 · doi:10.1177/1090198107305078

A Small Dose of HIV? HIV Vaccine Mental Models and Risk Communication

2007· article· en· W2126333613 on OpenAlexaff
Peter A. Newman, Danielle Seiden, Kathleen Johnston Roberts, Lisa Kakinami, Naihua Duan

Bibliographic record

VenueHealth Education & Behavior · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsCentre for Social InnovationUniversity of Toronto
Fundersnot available
KeywordsMedicineHIV vaccineThematic analysisMental healthPopulationFocus groupPublic healthAnxietyCondomQualitative researchFamily medicineHuman immunodeficiency virus (HIV)Environmental healthPsychiatryNursingVaccine trial

Abstract

fetched live from OpenAlex

Existing knowledge and beliefs related to HIV vaccines provide an important basis for the development of risk communication messages to support future HIV vaccine dissemination. This study explored HIV vaccine mental models among adults from segments of the population disproportionately affected by HIV/AIDS. Nine focus groups were conducted with participants (N = 99; median age = 33 years; 48% female; 22% African American, 44% Latino, and 28% white) recruited from needle exchange sites, public clinics, and gay community centers in Los Angeles. Data were analyzed using narrative thematic analysis and Ethnograph qualitative software. Mental models of HIV vaccines included live virus, side effects, complete protection (100% efficacy, lifetime protection, reduced anxiety about HIV/AIDS), and "high-risk groups." HIV vaccine risk communication to counter undue fears of vaccine-induced infection and side effects and to mitigate exaggerated expectations of a "magic bullet" may be vital to the effectiveness of first-generation HIV vaccines in controlling the AIDS epidemic.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.072
GPT teacher head0.393
Teacher spread0.320 · 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 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

Citations36
Published2007
Admission routes1
Has abstractyes

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