Cervical Cancer Screening amongst Female Nursing Students in a Tertiary Institution, in South South Nigeria
Bibliographic record
Abstract
INTRODUCTION: Developing countries have more challenges of cervical cancer among young women of reproductive age group. Good knowledge and practices of cervical cancer screening (CCS) among nursing students who graduate to become professional nurses can reduce the escalating incidence and high mortality among Nigerian women. METHODS: The study examined knowledge, attitude and practice of cervical cancer screening among female undergraduate nursing students in Department of Nursing Science, University of Calabar, Nigeria. Using simple random sampling technique a sample size of 212 nursing students was selected. Data was collected through a researchers developed and validated questionnaire titled Undergraduate nursing students knowledge attitude and practice of cervical cancer screening questionnaire (UNSKAPCCSQ). Simple frequencies and percentages were used to analyze data. RESULTS: Undergraduate nursing students had good knowledge (93.3%) of cervical cancer screening. The students exhibited poor attitude towards cervical cancer screening as only (26.7%) displayed positive attitude, while majority (73.3%) did not find it necessary to screen. Only (5%) had been screened for cervical cancer while (95%) did not undergo any screening test. CONCLUSION: Female undergraduate nursing students’ good Knowledge of cervical cancer screening did not translate to positive attitude and practice. Cervical cancer screening education should be intensified for nursing students. CCS should be a mandatory exercise for all newly admitted female undergraduate in the university. Nursing students should be made to participate actively in raising awareness on cervical cancer screening and management.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".