Evaluating societal preferences for human papillomavirus vaccine and cervical smear test screening programme
Bibliographic record
Abstract
BACKGROUND: Cervical cancer and genital warts are diseases associated with human papillomavirus (HPV) infection. Cervical smear testing is used as a cervical cancer screening tool in most countries worldwide. The newly introduced vaccines that prevent HPV infections are the quadrivalent vaccine (Gardasil), which prevents genital warts and cervical cancer, and the bivalent vaccine (Cervarix), which prevents cervical cancer only. Public preferences for HPV vaccines and smear test screening were determined using a discrete choice experiment. METHODS: Participants from across Canada completed a choice-based questionnaire to measure preferences from which willingness to pay (WTP) was calculated for the following: (1) lifetime risk of cervical cancer and genital warts, (2) frequency of smear testing, (3) need for vaccine booster, (4) target group to vaccinate, (5) frequency of side effects and (6) cost of the vaccine (from 2008). A mixed effect logistic model was used to analyse the data. RESULTS: Of the 1157 participants, the mean age was 44 years (SD 15) and 49% were women. Respondents preferred a vaccine that gave lifelong immunity, a vaccination programme that targeted boys and girls and a vaccine that gave protection from genital warts and cervical cancer. Respondents were averse to yearly smear testing. On average, respondents were willing to pay $C53 and $C22 to avoid a 1% increase in the risk of cervical cancer and genital warts, respectively. CONCLUSIONS: Society agrees with the introduction of the HPV vaccination programme, but would prefer a programme that targets boys and girls with the quadrivalent vaccine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".