Perspectives on living with ovarian cancer: Young women’s views
Bibliographic record
Abstract
Ovarian cancer is the fourth leading cause of cancer-related deaths in women. Ovarian cancer, and its treatment, has a considerable effect on the quality of life of women diagnosed with the disease. Young women diagnosed with ovarian cancer must confront life-threatening illness at a time when many are in the midst of raising children, maintaining a household, and actively engaging in work and career activities. Very little has been reported about the perspectives of young women regarding their experiences with ovarian cancer. This article reports data from 39 women 45 years of age or less concerning the impact of ovarian cancer and its treatment as well as the availability of support. At the time of the study, the women were, on average, 38 years of age and approximately two-thirds were married and had children. About half of the women were working. The most frequently identified problems included side effects (n = 25), fear of recurrence (n = 25), and difficulty sleeping (n = 25). On average, women reported experiencing 10.4 problems since diagnosis. Of those who experienced problems, less than 50% perceived they had received adequate help. Approximately two-thirds of these women experienced a lifestyle change. Quality of life was rated significantly lower following their experience with ovarian cancer. Implications for oncology nurses emerge in areas of assessment, referral, and patient teaching.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".