Cervical Cancer: Assessment of Its Knowledge, Utilization of Services and Its Determinant Among Female Undergraduate Students in a Low Resource Setting
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is a preventable disease that contributes significantly to the death of women. This study is aimed at determining the level of knowledge and utilization of cervical cancer screening and its determinants among female undergraduates of Ebonyi State University. METHODS: A structured questionnaire was used for a cross-sectional survey of the study population between January 1 and March 3, 2018. The data were analyzed using IBM SPSS Statistics version 20. Data were represented with frequency table, simple percentage, mode, range, Chi square and pie chart. The level of significance is at P-value < 0.05. RESULTS: Majority (74.8%) of the respondents were aware of cervical cancer and it could be prevented (70.8%). More than three-fifths (68.30%) were informed via health workers, and 86.8% were aware that post-coital vaginal bleeding is a symptom. Less than half (49.8%) knew that HPV is the primary cause, and only 32.9% were aware of the HPV vaccine. One-quarter of the respondent were aware that early coitarche is a risk factor for cervical cancer. Only 41.8% of the women were aware of Pap smear, 9.2% had undergone screening, and 97.6% were willing to be screened. Marital status was the significant determinant of being screened while class level did not significantly influence uptake of cervical cancer screening. The most common reason (20.6%) for not being screened was lack of awareness of the test. CONCLUSION: Our study population had a good knowledge of cervical cancer, but utilization of cervical cancer screening was poor. Awareness creation through the mass media and provision of affordable screening services can promote the use of cervical cancer screening in the study area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".