Sex Differences in Non-Nephrologist Care Providers' Knowledge and Perceptions of Female Reproductive Health in CKD: A Survey Protocol
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) affects 12% of females globally and has important implications for reproductive health. Despite recommendations for multidisciplinary reproductive care, many females with CKD are managed independently by non-nephrologist care providers. This study aims to assess sex differences in non-nephrologist care providers' knowledge and perceptions of reproductive health in females with CKD. Methods: A web-based survey will be developed following a literature review and consultations with experts in the field, as well as patient partners. Non-nephrologist care providers' knowledge and perceptions of reproductive health in females with CKD will be assessed, specifically as it relates to sexual health, menstruation, fertility, pregnancy, and menopause. Survey validity, clarity, and usability will be evaluated through pre-testing. The survey will be available in the world's 10 most commonly spoken languages. Snowball sampling will be employed via targeted emails to international and national reproductive healthcare provider organizations, as well as through social media platforms. Results: Numeric and Likert-scale survey responses will be descriptively analyzed and free-text survey responses will undergo conventional content analysis. All results will be stratified by sex. Conclusions: An improved understanding of sex differences in non-nephrologist care providers' knowledge and perceptions of female reproductive health in CKD can help identify gaps in care delivery and optimize reproductive health for this underserved population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| 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 | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".