Patient Preferences and Willingness to Pay for Cervical Cancer Prevention in Zambia: Protocol for a Multi-Cohort Discrete Choice Experiment
Bibliographic record
Abstract
BACKGROUND: Although most countries in southern Africa have cervical cancer screening programs, these programs generally fail to reach a significant majority of women because they are often implemented as pilot or research projects, and this limits their scope and ability to scale up screening. Some countries have planned larger-scale programs, but these have either never been implemented or have not been successfully scaled up. Most of the global burden of cervical cancer is experienced in countries with limited resources, and mortality from cervical cancer is the most common cause of cancer-related deaths among women in Sub-Saharan Africa. OBJECTIVE: The purpose of this study is to learn about preferences for cervical cancer screening in Zambia, to identify barriers and facilitators for screening uptake, and to evaluate willingness to pay for screening services to support the scaling up of cervical cancer screening programs. METHODS: We will conduct a discrete choice experiment by interviewing women and men and asking them to choose among constructed scenarios with varying combinations of attributes relevant to cervical cancer screening. To inform the discrete choice experiment, we will conduct focus groups and interviews about general knowledge and attitudes about cervical screening, perception about the availability of screening, stigma associated with cancer and HIV, and payment for health care services. For the discrete choice experiment, we will have a maximum design of 120 choice sets divided into 15 sets of 8 tasks each with a sample size of 320-400 respondents. We will use a hierarchical Bayesian estimation procedure to assess attributes at the following two levels: group and individual levels. RESULTS: The model will generate preferences for attributes to assess the most important features and allow for the assessment of differences among cohorts. We will conduct policy simulations reflecting potential changes in the attributes of the screening facilities and calculate the projected changes in preference for choosing to undergo cervical cancer screening. The findings from the discrete choice experiment will be supplemented with interviews, focus groups, and patient surveys to ensure a comprehensive and context-based interpretation of the results. CONCLUSIONS: Because willingness to pay for cervical cancer screening has not been previously assessed, this will be a unique and important contribution to the literature. This study will take into account the high HIV prevalence in Sub-Saharan Africa and prevailing gender attitudes to identify an optimal package of interventions to reduce cervical cancer incidence. This simulation of women's decisions (and men's support) to undergo screening will lay the foundation for understanding the stated preferences and willingness to pay to help design future screening programs. REGISTERED REPORT IDENTIFIER: RR1-10.2196/10429.
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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 | 0.055 | 0.041 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.054 | 0.006 |
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".