“I Wasn't Gonna Let It Stop Me”: Exploring Women's Experiences of Getting Through Chemotherapy for Ovarian Cancer
Bibliographic record
Abstract
BACKGROUND: Many women with ovarian cancer experience significant chemotherapy-related adverse effects during treatment and thus cannot complete it without dose reductions and/or delays. There is some indication that chemotherapy completion is associated with improved survival, although currently little is known about what helps women get through chemotherapy. OBJECTIVE: The aim of this study was to explore women's accounts of the factors they believed were helpful during their ovarian cancer treatment. METHODS: Using a qualitative approach within a critical realist framework, we conducted interviews with 18 women who had received chemotherapy for ovarian cancer and analyzed the data thematically. RESULTS: We identified 3 main themes related to women's experiences of dealing with chemotherapy: "optimistic tenacity," which illustrates a specific stoic identity that women assumed during treatment; "self-care," which reflects the health behaviors and activities women engaged in and lifestyle adjustments they made; and "support systems," which emphasizes the importance of social, emotional, and medical support and the specific needs shared by women undergoing treatment for ovarian cancer. CONCLUSIONS: Our findings contribute to a deeper understanding of women's unique experiences of treatment that may influence whether they complete chemotherapy for ovarian cancer. IMPLICATIONS FOR PRACTICE: This study highlights the central role of women's optimistic determination within a wider self-caring and well-supported context of treatment; we aim to provide feedback and guidance to health professionals caring for women with ovarian cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".