Willingness to Pay for Breast Cancer Screening: A Systematic Review
Bibliographic record
Abstract
Background: Women's breast cancer was identified as the primary contributor to global cancer cases. This condition puts a significant economic burden on both society and the healthcare system, with the highest expenses related to medical treatment costs.Objectives: The study aims to review the willingness to pay (WTP) for breast cancer screening and the factors influencing it.Methods: Four databases (Scopus, PubMed, ProQuest, and Google Scholar) were used to search related articles that mentioned the WTP for breast cancer screening. Preferred Reporting Items for Systematic Reviews (PRISMA) guideline was used to execute this systematic review study. Finally, 11 articles were included in this study. The quality of reviewed studies was assessed using a JBI cross-sectional study and a JBI quasi-experimental study.Results: The study found that the WTP value for breast cancer screening varied from $1 to $500 in Iran, Malaysia, Singapore, the US, and Canada. Most studies explained that willingness to pay and willingness to take breast cancer screening were affected by sociodemographic aspects such as family history of ovarian or breast cancer, income status, education status, age, and marital status.Conclusion: Governmental authorities should consider the implementation of a breast cancer screening program or explore cost-sharing mechanisms for breast screening, to mitigate the incidence of breast cancer.
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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 | 0.011 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".