Student nurses’ practices and willingness to teach relatives breast self-examination in Nigeria
Bibliographic record
Abstract
Background: Breast cancer is the most common cancer and the leading cause of cancer-related death for women worldwide. Breast self-examination (BSE) is an essential, low-cost, and simple tool for detecting breast cancer early. Employing the idea of 'charity begins at home' by involving student nurses in teaching BSE to relatives will improve early detection. Aim: To assess nursing students' practice and willingness to teach BSE to their relatives. Setting: A college of nursing and midwifery in one state under North-Central Nigeria. Methods: -value of 0.05 were conducted. Results: Respondents indicated where they learned about BSE. There were 98.5% respondents who had heard about BSE, and 89.8% of them had good practice of BSE. However, a quarter did not teach BSE to relatives. There were no statistically significant associations noted. Conclusion: Most of the nursing students were aware of BSE and knew how to perform it, although a quarter did not teach BSE to their relatives. Therefore, it may be necessary to sensitise nurses to cultivate the habit of teaching BSE to relatives and women in the community. Contribution: It is crucial to provide nurses with the skills and knowledge required to carry out BSE effectively, as well as teach women how to perform it on themselves, to improve breast cancer detection rates in Nigeria.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".