Addressing Factors Associated with Arab Women's Socioeconomic Status May Reduce Breast Cancer Mortality: Report from a Well Resourced Middle Eastern Country
Bibliographic record
Abstract
Differences in socioeconomic status (SES) such as income levels may partly explain why breast cancer screening (BCS) disparities exist in countries where health care services are free or heavily subsidized. However, factors that contribute to such differences in SES among women living in well resourced Middle East countries are not fully understood. This quantitative study investigated factors that influence SES and BCS of Arab women. Understanding of such factors can be useful for the development of effective intervention strategies that aim to increase BCS uptake among Arab women. Using data from a cross-sectional survey among 1,063 Arabic-speaking women in Qatar, age 35+, additional data analysis was performed to determine the relationship between socioeconomic indicators such as income and other factors in relation to BCS activities. This study found that income is determined and influenced by education level, occupation, nationality, years of residence in the country, level of social activity, self-perceived health status, and living area. Financial stress, unemployment, and unfavorable social conditions may impede women's participation in BCS activities in well resourced Middle East countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".