Knowledge and practice of breast-self examination among female undergraduate students of Ahmadu Bello University Zaria, Northwestern Nigeria
Bibliographic record
Abstract
BACKGROUND: Carcinoma of the breast is an important public health problem in Nigeria and studies have reported low levels of awareness and practice of breast self examination as an important method of prevention. Breast self examination is a cost-effective method of early detection of cancer of the breast especially in resource poor countries. We assessed knowledge and practice of breast-self examination (BSE) among female undergraduate students of Ahmadu Bello University Zaria, Nigeria. METHOD: In this study, knowledge and practice of BSE were examined among 221 female students aged 16-28 years old studying at Ahmadu Bello University Zaria using self administered questionnaires. RESULTS: It was found that despite nearly three quarter of the respondents (87.7%) had heard of BSE, only 19.0% of them were performing this examination monthly. Regarding the sources of information about BSE among respondents, media was found to be most common followed by health workers accounting for 45.5% and 32.2% respectively. Regular performance of BSE was significantly correlated with duration of stay in the University (X2 = 81.9, df = 3, P 2 = 17.4, df = 2, P CONCLUSION: We observed a disparity between high levels of knowledge of BSE compared to a low level of practice. Public health education using the media could significantly reduce the knowledge-practice gap and early detection of breast lump.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".