Knowledge and Beliefs Toward Mammography Screening Among Jordanian Women: Cross-Sectional Study
Bibliographic record
Abstract
Background: Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer-related deaths among women globally. Despite the significance of mammography screening rate for early BC detection among Jordanian women, it remains low, mainly due to various cognitive, psychosocial, and behavioral barriers. Understanding these factors is essential for developing effective interventions. Objective: This study aims to assess the BC knowledge and beliefs about mammography screening among Jordanian women aged 40 years and older based on the Health Belief Model (HBM) as a theoretical framework. Methods: A cross-sectional design was used with a convenience sample of eligible women from a Jordanian public hospital. Data were collected through face-to-face interviews using a validated Arabic structured questionnaire consisting of 3 sections: sociodemographic data, knowledge about BC, and health beliefs about mammography. Descriptive statistics and multivariate analysis of variance (MANOVA) were conducted using IBM SPSS version 28. Results: A total of 405 women completed the study, with an average (SD) age of 52.4 (8.57) years. Findings revealed a notably low knowledge level, as participants scored an average (SD) of 5.80 (2.64) out of 12. The average (SD) scores for the health beliefs section (out of 5) were also low: perceived benefits, 2.59 (0.59); perceived barriers, 2.48 (0.71); and health motivation, 2.51 (0.71). Significant associations (P<.001) with medium to large effect sizes (ηp²>0.06) were observed between participants' age and education level in relation to BC knowledge and health beliefs regarding mammography screening. Participants cited several reasons for their reluctance to undergo mammography, including a lack of knowledge (72.8%), cultural beliefs (63%), and religious factors (29.4%). Conclusions: A significant gap exists in BC knowledge and beliefs about mammography among Jordanian women aged 40 years and older. Policy makers and health care providers should prioritize the development of tailored strategies and context-specific, sensitive educational interventions. These efforts should address the unique needs, cultural beliefs, and awareness levels of this population to improve mammogram screening practices in Jordan.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".