A Narrative Review of the Roles of Nursing in Addressing Sexual Dysfunction in Oncology Patients
Bibliographic record
Abstract
Sexual dysfunction affects an estimated 50-70% of cancer survivors but remains underrecognized and undertreated, impacting quality of life and emotional well-being. This narrative review involves a comprehensive search of PubMed/MEDLINE, CINAHL, Scopus, Web of Science, and ScienceDirect for English-language publications (January 2010-May 2025), using combined MeSH and free-text terms for 'sexual health', 'cancer', 'nursing', 'roles of nurses', 'immunotherapy', 'targeted therapy', 'sexual health', 'sexual dysfunction', 'vaginal dryness', 'genitourinary syndrome of menopause', 'sexual desire', 'body image', 'erectile dysfunction', 'climacturia', 'ejaculatory disorders', 'dyspareunia', and 'oncology'. We used the IMRAD (Introduction, Methods, Results, and Discussion) approach to identify 1245 records and screen titles and abstracts. Fifty studies ultimately met the inclusion criteria (original research, reviews, and clinical guidelines on oncology nursing and sexual health). Results: All the treatments contributed to reduced libido, erectile dysfunction, dyspareunia, and body image concerns, with a prevalence of 57.5% across genders. Oncology nurses can provide sex education and counseling. Barriers (limited training, cultural stigma, and the absence of protocols) hinder effective intervention. Addressing these issues through sexual health curricula, formal referral systems, and policy reforms can enhance nursing care. Future research should assess the impact of targeted nurse education and the institutional integration of sexual health into cancer care.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".