Gender and decistion making in Neurology, an Argentinian study. (P5-12.005)
Notice bibliographique
Résumé
Objective: Determine the distribution of hierarchical positions in neurology according to gender in Argentina Background: Even though in Argentina, 7 out of 10 medical students are women, 75% of postgraduate hierarchical positions are held by men. The representation of women in senior positions in Argentine neurology is unknown. Design/Methods: A cross-sectional study was carried out, an online form was sent to neurologists throughout Argentina. Responses were received from May to July 2021. The questionnaire included age, gender, family constitution, type of training in neurology, managementtraining, postgraduate degrees, type of institution (public or private) and desire to occupy a position of decision making. The T and Chi square test were used to compare continuous and categorical variables, respectively. Logistic regression was used to determine the association of multiple variables. Results: 383 responses were received, 63% were women from 23/24 provinces of Argentina. Women were younger than the men (41.9 ± 9.9 vs 44.9 ± 11.7 years, p = 0.009). The hierarchical positions were significantly lower in women than in men 25.5 vs 50.7%, p <0.001). There was a significant association between high-ranking positions and gender [OR 0.36 (CI95% 0.21–0.60), p <0.001], age [OR 1.07 (CI95% 1.04–1.10), p <0.001)], the desire to occupy that position [OR 3.48 (95% CI 1.95–6.21), p <0.001], management training [OR 1.88 (95% CI 1.01–3.51), p = 0.046] and postgraduate degree (OR 2.50 (95% CI 1.47–4.25), p = 0.001). Within young neurologists, hierarchical positions were directly correlated with age and inversely correlated with female gender, while in neurologists older than 55 years, age was not correlated with hierarchical positions, while gender inequities persisted. Conclusions: In Argentina, most senior positions are held by men. The female gender was associated with a lower probability of occupying decision-making positions regardless of other variables Disclosure: Maria Gaitan has received personal compensation in the range of $500-$4,999 for serving as a has received reimbursement for developing educational presentations and/or travel/accommodations stipends from with Merck SA, Merck-Serono Argentina, Biogen-Idec Argentina, Novartis Argentina, Roche Argentina,. Celica Ysrraelit has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Biogen. Celica Ysrraelit has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Merck. Celica Ysrraelit has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Genzyme. Celica Ysrraelit has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Roche. Celica Ysrraelit has received personal compensation in the range of $500-$4,999 for serving as an Expert Witness for Merck, . Celica Ysrraelit has received personal compensation in the range of $500-$4,999 for serving as an Expert Witness for Biogen. Celica Ysrraelit has received personal compensation in the range of $500-$4,999 for serving as an Expert Witness for Bayer. Celica Ysrraelit has received research support from Novartis. Laura Negrotto has received personal compensation for serving as an employee of Merck. Laura Negrotto has received personal compensation in the range of $0-$499 for serving as a Consultant for Entelai. Dr. Bruno has received personal compensation in the range of $0-$499 for serving as a Consultant for Biogen. The institution of Dr. Bruno has received research support from Paladin Labs. The institution of Dr. Bruno has received research support from Ipsen. Dr. Fernandez has nothing to disclose. Dr. Azcona has nothing to disclose. Maria Justich has nothing to disclose. Mariela Cabrera has nothing to disclose. Mrs. Rodriguez Perez has nothing to disclose. Marina Romano has nothing to disclose. Dr. Ibarra has nothing to disclose. Dr. Derosa has nothing to disclose. Guadalupe Bruera has nothing to disclose. Ms. Zavala has nothing to disclose.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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 ».