Awareness of working women in Mansoura University about ovarian cancer: An intervention follow-up study
Bibliographic record
Abstract
Background and Aim : Ovarian cancer causes more death than any other gynecologic tumor, other than it accounts for about 3% of all cancers in women. Aim: This study was aimed to raise the awareness of working women in Mansoura University about ovarian cancer. Methods : Study Design: A quasi-experimental one group pre-post test design was utilized. Setting: The study was conducted in different faculties of Mansoura University. Subjects: The study subjects included 199 working women in different faculties of Mansoura University distributed as: 92 women from practical faculties, 33 from theoretical faculties and 74 from medical faculties using a stratified random sample. Tool: Self-administered questionnaire consists of three different parts. The first part includes socio-demographic characteristics of working women, the second part includes the source of knowledge about ovarian cancer and the third part includes the working women knowledge about ovarian cancer. Results : About 46.7% of the study sample their age ranged from 35 to less than 50 years. About 41.2% among the study group did not have any source of knowledge about ovarian cancer. The study participants had poor knowledge about the ovarian cancer manifestation, risk factor, treatment and prevention in the pre test that improved after the educational session. Conclusions : The health education session about ovarian cancer showed a significant effect in the form of a remarkable increase in the participants' level of knowledge about the disease. Thus, health education about ovarian cancer should be adopted as an element of the services offered to the working women.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".