One Year of Women in Nephrology India: Where Do We Stand and Where Are We Headed?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».