Development of a Comprehensive Model for Cancer Symptom Care for Women With Ovarian or Endometrial Cancer
Bibliographic record
Abstract
Background: Women with ovarian or endometrial cancer experience multiple symptoms during chemotherapy. Specialized cancer nurses possess specific knowledge and competencies to effectively monitor and manage treatment-related toxicities and provide self-management support. Objective: To describe the conception and development of a comprehensive cancer symptom model of care in an oncological setting for women diagnosed with ovarian or endometrial cancer. Methods: The participatory evidence-based, patient-focused process for guiding the development, implementation, and evaluation of advanced practice nursing roles—the participatory, evidence-based, patient-centered process for advanced practice (PEPPA) framework directed the process. The first 6 steps of this 9-step framework were utilized to incorporate research evidence, engage, and obtain the input of key stakeholders. Results: Stakeholders (n = 27) contributed with specific knowledge, perspectives, and feedback to the entire development process, and several needs were identified. Following structured discussions, a new model of cancer symptom care with elements such as symptom management, electronic patient-reported outcomes, and an expanded nursing role in the form of nurse-led consultations was developed. Conclusions: We effectively utilized the PEPPA framework to design a new cancer symptom model of care, that was agreed upon by key stakeholders. Implications for Practice: This stakeholder-engaged, and evidence-driven process could be used as a template for others wanting to develop a population-specific model of care to improve cancer symptom management. What is Foundational: With the expansion of the cancer nursing role, the new model has the potential to improve the quality of cancer care and health outcomes related to symptom management.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".