The needs of gynecological cancer survivors at the end of primary treatment: A scoping review and proposed model to guide clinical discussions
Bibliographic record
Abstract
OBJECTIVE: Gynecological cancer (GC) survivors have unmet needs when they complete primary cancer treatment. Despite this, no known research has summarized these needs and survivors' suggestions to address them. We conducted a scoping review to fill these gaps and develop a model useful to guide clinical discussions and/or interventions. METHODS: English, full length, and accessible primary studies describing the needs of GC survivors were included. No restrictions on date nor country of publication were applied. Two reviewers screened and extracted data, which was verified by a third reviewer. RESULTS: Seventy-one studies met the inclusion criteria for data extraction. Results were thematically grouped into seven dimensions: physical needs, sexuality-related concerns, altered self-image, psychological wellbeing, social support needs, supporting the return to work, and healthcare challenges and preferences. After consulting with a stakeholder group (a GC survivor, clinicians, and researchers), the dimensions were summarized into a proposed model to guide clinical assessments and/or interventions. CONCLUSION: Results illuminate the diverse needs of GC survivors as they complete primary cancer treatment and their recommendations for care to meet these needs. PRACTICE IMPLICATIONS: The resulting model can be used to guide assessments, discussions and/or interventions to optimally prepare GC survivors for transition out of primary cancer treatment.
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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 | 0.071 | 0.106 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| 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".