Use of conjoint analysis to assess HIV vaccine acceptability: feasibility of an innovation in the assessment of consumer health-care preferences
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".