Coping With Ovarian Cancer: Do Coping Styles Affect Outcomes?
Bibliographic record
Abstract
UNLABELLED: The majority of patients with ovarian cancer face a long road of persistent hardship and strain. Treatment of this disease is intense, involving aggressive debulking surgery and multiple chemotherapy regimens. Coping with the disease and its treatment challenges patients on many levels. This review was developed to summarize the evidence concerning the impact of coping strategies on outcomes in patients with ovarian cancer. A comprehensive search of the literature in the field of coping and ovarian cancer was undertaken. Using the Ovid interface, 3 electronic databases, including Medline, Cinahl, and PsycINFO, were searched using the search terms "coping," "cancer," and "ovarian cancer." In addition, a critical appraisal of the 2 most widely used scales to assess coping strategies was a component of this work. This review highlights the relative lack of knowledge on coping in ovarian cancer, the methodologic challenges to its study, and the need to develop an instrument that is tailored to evaluate coping strategies used by patients with ovarian cancer. A validated instrument to assess coping strategies used by patients with ovarian cancer is needed. Identification of strategies that are maladaptive or destructive in patients with ovarian cancer could be used to improve quality of care for patients burdened by this disease. TARGET AUDIENCE: Obstetricians & Gynecologists, Family Physicians. LEARNING OBJECTIVES: After completion of this article, the reader should be able to list the potential coping strategies for patients with ovarian cancer, to explain the various coping assessment scales, and to summarize the evidence concerning the impact of coping strategies on outcomes in ovarian cancer patients.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".