Do socioeconomic factors influence breast cancer screening practices among Arab women in Qatar?
Bibliographic record
Abstract
OBJECTIVES: Breast cancer incidence rates are rising in Qatar. Although the Qatari government provides subsidised healthcare and screening programmes that reduce cost barriers for residents, breast cancer screening (BCS) practices among women remain low. This study explores the influence of socioeconomic status on BCS among Arab women in Qatar. SETTING: A multicentre, cross-sectional quantitative survey was conducted with 1063 Arab women (87.5% response rate) in Qatar from March 2011 to July 2011. Women who were 35 years or older and had lived in Qatar for at least 10 years were recruited from seven primary healthcare centres and women's health clinics in urban and semiurban regions of Qatar. Associations between socioeconomic factors and BCS practice were estimated using χ(2) tests and multivariate logistic regression analyses. RESULTS: Findings indicate that less than one-third of the participants practised BCS appropriately, whereas less than half of the participants were familiar with recent BCS guidelines. Married women and women with higher education and income levels were significantly more likely to be aware of and to practise BCS than women who had lower education and income levels. CONCLUSIONS: Findings indicate low levels of awareness and low participation rates in BCS among Arab women in Qatar. Socioeconomic factors influence these women's participation in BCS activities. The strongest predictors for BCS practice are higher education and higher income levels. RECOMMENDATIONS: Additional research is needed to explore the impact of economic factors on healthcare seeking behaviours in the Middle Eastern countries that have a high national gross domestic product where healthcare services are free or heavily subsidised by the government; promotion of BCS and intervention strategies in these countries should focus on raising awareness about breast cancer, the cost and benefit of early screening for this disease, particularly among low-income women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".