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Record W2114029108 · doi:10.1258/ijsa.2011.011189

Use of conjoint analysis to assess HIV vaccine acceptability: feasibility of an innovation in the assessment of consumer health-care preferences

2012· article· en· W2114029108 on OpenAlexaffabout
S J Lee, Peter A. Newman, W. Scott Comulada, William E. Cunningham, Naihua Duan

Bibliographic record

VenueInternational Journal of STD & AIDS · 2012
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsCentre for Social InnovationUniversity of Toronto
FundersNational Institute on Minority Health and Health DisparitiesNational Institute of Mental HealthNational Institute on Aging
KeywordsConjoint analysisMedicineSample (material)Duration (music)PopulationHuman immunodeficiency virus (HIV)HIV vaccineHealth careEnvironmental healthFamily medicineMarketingPreferenceEconomic growthBusinessVaccine trial

Abstract

fetched live from OpenAlex

Engaging consumers in prospectively shaping strategies for dissemination of health-care innovations may help to ensure acceptability. We examined the feasibility of using conjoint analysis to assess future HIV vaccine acceptability among three diverse communities: a multiethnic sample in Los Angeles, CA, USA (n = 143); a Thai resident sample in Los Angeles (three groups; n = 27) and an Aboriginal peoples sample in Toronto (n = 13). Efficacy had the greatest impact on acceptability for all three groups, followed by cross-clade protection, side-effects and duration of protection in the Los Angeles sample; side-effects and duration of protection in the Thai-Los Angeles sample; and number of doses and duration of protection in the Aboriginal peoples-Toronto sample. Conjoint analysis provided insights into universal and population-specific preferences among diverse end users of future HIV vaccines, with implications for evidence-informed targeting of dissemination efforts to optimize vaccine uptake.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.173
GPT teacher head0.489
Teacher spread0.316 · 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

Citations30
Published2012
Admission routes2
Has abstractyes

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