Breast Cancer Myths, Mysterious Miracles and Mistrust among Rural Womenfolk in Sarawak
Bibliographic record
Abstract
BACKGROUND: Sarawak hospitals report high rates of advanced cancer among women in remote rural areas. Cultural beliefs, low awareness, socio-economic challenges, geographical barriers, and the lack of specialist cancer care contribute to late diagnoses, particularly in breast cancer, exacerbating disparities in access to timely treatment. OBJECTIVE: This study aimed to explore cultural beliefs, barriers, and healthcare access challenges influencing breast cancer awareness and screening among Sarawak's indigenous and rural communities. METHODS: This qualitative study explores how rural Sarawakian women perceive breast cancer and their access to healthcare. Twenty women from three main geographical terrains in Borneo-coastal, riverine, and highland areas participated in the study. After informed consent was obtained, semi-structured interviews were conducted. Data was coded and thematically analyzed to identify cultural nuances affecting their knowledge. RESULTS: The study found a strong link between cancer and negative beliefs rooted in sociocultural backgrounds. Myths varied, but most associated cancer with death, a fatalistic culture relying on spiritual faith for healing. Participants delayed seeking medical help until "the pain is unbearable," often using oils and herbs first. Lack of knowledge and lack of access to information about cancer are two main findings from the study. Although the majority of the respondents were not equipped with adequate information, they expressed interest in learning about breast screening programs and attending breast cancer awareness campaigns. CONCLUSION: The findings will be used to design behavioral intervention modules to educate rural Sarawak populations about the importance of breast self-examination (BSE) practices, early detection, and screening.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".