Socioeconomic Inequalities in Uptake of Breast Cancer Screening among Saudi Women: A Cross-Sectional Analysis of a National Survey
Bibliographic record
Abstract
Timely and adequate screening for breast cancer could improve health outcomes and reduce health costs. However, the utilization of free breast cancer screening services among Saudi women is very low. This study aims to investigate socioeconomic inequalities in breast cancer screening among Saudi women. The data of this study were extracted from the nationally representative Saudi Health Interview Survey, conducted in 2013; the study included 2786 Saudi women. Multivariate logistic regression, the concentration curve, and the concentration index were used to examine, illustrate, and quantify income- and education-related inequalities in three outcomes: Knowledge about self-breast examination (SBE), clinical breast examination (CBE) received in the last year, and mammography, that has ever been previously carried out. Results showed a marked socioeconomic gradient in breast cancer screening services. The concentration index by income was 0.229 (SBE), 0.171 (CBE), and 0.163 (mammography). The concentration index by education was 0.292 (SBE), 0.149 (CBE), and 0.138 (mammography). Therefore, knowledge about breast cancer screening, and the utilization of screening services, were more concentrated among richer and better-educated women. Poorer and less educated women had less knowledge about self-breast examination, and had considerably less adherence to clinical breast examination and mammography. The findings are helpful for policy makers to devise and implement strategies to promote equity in breast cancer screening among Saudi women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".