Understanding breast health awareness in an Arabic culture: qualitative study protocol
Bibliographic record
Abstract
AIM: To explore breast health awareness and the early diagnosis and detection methods of breast cancer from the perspective of women and primary healthcare providers in the Jizan region of the Kingdom of Saudi Arabia. BACKGROUND: Although there is a high incidence of advanced breast cancer in young women in the Kingdom of Saudi Arabia, there is no standardized information about breast self-examination, or is there a national screening programme involving clinical breast examination and mammography available. DESIGN: Qualitative exploratory study. METHODS: Data collection will consist of 36 face-to-face semi-structured interviews: 12 with general practitioners; 12 with nurses at primary healthcare centres and with 12 women who attend the health centres. This study will be carried out in eight states across the Jizan region (four rural and four urban) to reflect the cultural diversity of Jizan. The data will be analysed using thematic content analysis. Research Ethics Committee approval was obtained in June 2015. DISCUSSION: While we understand the enablers and barriers to breast health awareness outside of Saudi culture, in the Kingdom of Saudi Arabia, particularly in rural populations such as Jizan, there is a lack of research. This study will add positively to the international knowledge base of this topic. The findings will give evidence and inform policy about women and healthcare providers' experiences in Jizan, in a society where such topics are taboo.
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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 | 0.025 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".