Uptake of breast cancer screening methods: perspectives of members of staff of Federal Medical Centre, Abeokuta
Bibliographic record
Abstract
Introduction: Breast cancer (BC) was ranked the most common among the top ten malignancies in 2022, evidenced by high incidence and rates rapid mortality and morbidity rates in Nigeria. BC screening method (BCSM) helps to discover BC early, gives more treatment options and raises cancer survival rates. Little is known about the utilisation of BCSM in this community, which prompted this study. Objective: This study was conducted among the staff of the Federal Medical Center, Abeokuta, and it assessed their knowledge, attitudes and use of BCSM. Methods: This study selected 270 staff members using a descriptive cross-sectional method and a convenience sampling technique. Data were analysed using the Statistical Package for Social Sciences version 25.0. Hypotheses were tested using chi-square, multiple linear regression and Pearson correlation coefficient at a 0.05 level of significance. Results: The study's results showed a high BCSM knowledge level of 71.9% but a low utilisation level of 57.8%; however, there was a positive attitude towards utilisation. Additionally, there was a significant relationship between staff members' gender, age, educational qualifications, department and both their knowledge and utilisation of BCSM (p < 0.05). The Pearson correlation revealed a positive trend between knowledge and utilisation. Conclusion: BCSM offers an opportunity for early detection, diagnosis and disease prevention of BC; it also serves as an avenue to inform and enlighten people on important health issues, including health promotion activities and screening as they pertain to BC. More BC awareness programs are advocated to educate people on the importance of BC Screening to enhance early detection and treatment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".