One Year of Women in Nephrology India: Where Do We Stand and Where Are We Headed?
Bibliographic record
Abstract
Background: Undoubtedly, women have made outstanding achievements in various areas such as education, clinical practice, interventions, and research and thus, have contributed significantly to the field of nephrology across the globe. Women in Nephrology India (WIN-India) is an organization established in August 2021 to provide mentorship and a support system with its diverse goals in the arena of nephrology. Throughout the year the organization has conducted multiple academic activities in the form of live webinars, quizzes, symposiums, clinical case discussions and newsletters. Methods: We evaluated the status of WIN-India in the country and the quality of content of academic activities that WIN-India conducted from August 2021 to April 2022. Study participants were invited to take part in the study using Survey Monkey, an online survey collection tool. The participants of this study were faculty of nephrology, nephrology residents, dieticians, dialysis technicians, and nurses. The survey included 16 questions of which 4 were related to demographic variables, one was for suggestions and weaknesses. The remaining questions elicited the quality of the content of academic activities and the newsletter (on a scale1-10). Results: A total of 250 responses were received. 225 respondents (90%) were aware of WIN-India. The most common age group of respondents was 25-35 years, and 60% belonged to male sex. The majority of participants were nephrology faculty (50% private practitioners, 36.6% academic nephrologists, and 10% were trainees), Social media was the most popular source for creating awareness about WIN-India. On a scale of 1-10, academic content of the education of WIN-India and letter was reported as 10 by 35% and 30% and 9 by 38% and 35%, of the respondents respectively. 62% of the respondents reported that WIN-India webinars were beneficial in their clinical practice and research projects and 83% felt that WIN-India is a step forward toward improving nephrology education. 80% of individuals were interested in participating in WIN-India activities. The main feedback was to increase social media coverage to enhance its outreach while others felt that there should be no gender bias. Conclusions: The findings of the study showed that WIN-India successfully provided a platform for academics, mentoring, networking, advocacy, and the development of leadership.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".