Structural equation modeling of factors influencing women’s attitudes, comfort and willingness toward risk-stratified breast cancer screening
Bibliographic record
Abstract
Risk-stratified breast cancer screening has been proposed as an alternative to age-based screening programs, though its implementation may face challenges and requires support from stakeholders, particularly women. This study used structural equation modeling (SEM) to identify personal factors influencing women's attitudes, comfort level, and willingness towards risk-stratified screening. Factors analyzed included sociodemographic variables, general health, breast cancer risk perception, screening, and genetic testing history. Three models were tested to assess the direct and indirect effects of statistically significant factors. None of the outcomes were significantly associated with women's perceived health or history of genetic testing (all p > 0.05). A history of mammography was found to mediate the relationships between age, perceived risk, and personal breast cancer history with the outcomes. Income also mediated the relationships between education, employment, marital status, and the outcomes. A history of mammography and higher income were significantly associated with more favorable attitudes (β_mammo = 0.157; β_income = 0.098), greater comfort (β_mammo = 0.425; β_income = 0.134), and higher willingness (β_mammo = 0.471; β_income = 0.198) towards risk-stratified screening. In contrast, non-white ethnicity and older age were linked to less favorable attitudes (β_ethnicity = - 0.117; β_age = - 0.071), lower comfort (β_ethnicity = - 0.104; β_age = - 0.269), and decreased willingness (β_ethnicity = - 0.142; β_age = - 0.295). This study identified key factors influencing the acceptability of risk-stratified breast cancer screening that could be targeted to facilitate its implementation.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".