Examining the Knowledge and Practices of Nursing Care on Women with Endometriosis in Cyprus
Bibliographic record
Abstract
Endometriosis is a chronic gynecological disorder affecting approximately 10% of reproductive-aged women globally, leading to significant pain, infertility, and diminished quality of life. Despite advancements in diagnosis and management, knowledge gaps among nursing professionals continue to hinder effective patient-centered care. This study examines the knowledge and practices of nursing care for women with endometriosis in Cyprus, a country where cultural stigmas and disparities in women's health training may impact healthcare delivery. Using a systematic review methodology, relevant peer-reviewed studies from databases such as PubMed, Scopus, and CINAHL were analyzed based on inclusion and exclusion criteria. A PRISMA framework guided article selection, while a random effects model meta-analysis assessed trends in nursing competence and practice variability. Findings indicate that many Cypriot nurses lack adequate knowledge of endometriosis pathophysiology, symptom management, and multidisciplinary approaches, leading to delays in diagnosis and inconsistent care. Furthermore, continuing professional development (CPD) opportunities specific to endometriosis remain limited, contributing to nurses’ reliance on generalized gynecological knowledge. Cultural barriers also constrain open discussions about menstrual health, further complicating early intervention efforts. The study highlights the need for structured educational programs, standardized nursing protocols, and culturally sensitive communication strategies to improve care quality. Future research should explore the long-term impact of targeted training on nursing competency and patient outcomes. Addressing these gaps will strengthen the role of nurses in endometriosis management and enhance healthcare experiences for affected women in Cyprus.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".