Perceptions of Arab men regarding female breast cancer screening examinations—Findings from a Middle East study
Bibliographic record
Abstract
OBJECTIVES: In the Middle East, Qatar in particular, the incidence of breast cancer has substantially increased in recent years, and is expected to double by 2030. This diagnosis also occurs at a later stage in the disease. Early detection along with proper treatment reduces radical mastectomy and mortality rates, yet only one-third of Arab women in Qatar participate in breast cancer screening (BCS) activities of any sort. Many women in the conservative Qatari society rely on male family members for support and protection. This study investigates the attitudes and perceptions of Arab men in regards to breast cancer screening and what they see as both incentives and barriers to women's participation in BCS activities. DESIGN: A qualitative methodology using purposive sampling technique was chosen in order to explore participant's attitudes, beliefs and health-related actions. Individual in-depth interviews with open-ended questions were conducted with 50 Arab men during October 2011 to May 2012. Data collection, analysis, and interpretation occurred simultaneously. NVivo 9, a qualitative data analysis software program was used to organize themes and subthemes. RESULTS: It was found that most men understood the importance of regular BCS in early detection of breast cancer. They felt they had an important role in encouraging the women in their lives to participate in BCS activities, but were adamant that any examination must be done by a female health care professional. Few knew details about screening guidelines in Qatar, but most had a basic knowledge of some screening activities. Most indicated an interest in learning more about BC and screening activities in order to better help and inform their female family members. CONCLUSION: Because Arab men perceive that their opinions and support are a major factor influencing female family members' participation in breast cancer screening, it is important that any program instituted to increase such screening participation be aimed at both men and women. More information is needed by both sexes as to the need for and benefits of regular screening activities, the techniques used, and the newly revised guidelines in Qatar. Such a program needs to be introduced in the near future in order to avert, at least partially, the expected doubling in breast cancer cases by 2030 in the Middle East.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".