Perceptions of Gynecologic Health and Health Care in Females Living with CKD: A Survey Protocol
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) affects more than 12% of females globally and its prevalence is increasing rapidly. CKD is often accompanied by gynecological health complications, with implications for menstruation, menopause, sexual health, fertility, and pregnancy. However, patient perceptions of gynecologic health and healthcare in females living with CKD are poorly understood. This study aims to assess the perceptions of gynecologic health and healthcare of females living with CKD. Methods: An exploratory web-based survey will be developed following a thorough literature review and after consulting with experts in the fields of nephrology and gynecology, and with the engagement of patient partners. The questions will address patient perceptions of gynecologic health and healthcare-related to menstruation, menopause, sexual health, fertility, pregnancy, and urinary incontinence. Survey pre-testing with patient partners will assess face validity, clarity, length, usability, and technical functionality. The web-based survey will utilize adaptive questioning to help reduce survey complexity. Self-identified adult females with CKD will be invited to participate through national and international CKD patient organizations, as well as through social media platforms. Results: Numeric responses will be analyzed using descriptive statistics and open-ended responses will be analyzed using conventional content analysis. Conclusions: This study will be the first to our knowledge to assess comprehensive gynecologic health and healthcare-related perceptions of females living with CKD, and will aid in the integration of patient perspectives into future research and clinical initiatives.
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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 | 0.022 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".