Notice bibliographique
Résumé
At the 2023 Society of Neurological Surgeons meeting in Dallas, I heard post-graduate year 3 resident Remi Wilson give a talk about diversity, inclusion, and underserved populations. She made me think about the concept of “belonging,” a term representing the incorporation of the best elements of inclusion, especially as we focus and work toward promoting equity and diversity in our authors, reviewers, editors, and readers. I remembered when my first publication was accepted in a major neurosurgical journal. It was 1986 and it was the Journal of Neurosurgery. That acceptance letter said to me that “I belonged.” What I had written was believed by the field to be worthy of inclusion, worthy of reading by serious neurosurgeons, and worthy of adding to the archived foundation of our specialty. I could not yet say that these were “my peers” as I was only a junior resident, which made it even more special. I was being welcomed in. I belonged. It was such a wonderful emotion. I really could not get enough of it. I was growing year to year up the residency ladder, travelling to meetings both local and nationally, speaking to my new community, and soon having to think about a permanent position as a faculty neurosurgeon. What would I offer? To which subspecialty community would I want to join? In what cities would there be opportunities for fellowship or a permanent job? Where did I want to belong? Would I belong when I got there? Of course, as a Caucasian male, my path was a familiar one and I could see countless examples of others who had succeeded along such a route. Two of my coresidents were women, Beverly Walters and Anita North, who were the second and third women either completing or in-training at the University of Toronto when I started in 1985. They each were older than I, and both had participated in advanced postgraduate studies and had life experiences far beyond my meager few years after a quick college and medical school education. Unlike mine, their paths were not routine. Dr Walters was recently honored as the Schneider Lecturer at the 2023 meeting of the American Association of Neurological Surgeons. Back in residency, I wonder if these 2 women felt like they “belonged.” I do know that some of the faculty were especially tough on them and I saw that from my own vantage point. Every training program is working to become more diverse, and the medical evidence indicates that patients are often served better if their physician “looks like them,” “sounds like them,” or personally understands their heritage.1,2 Sometimes this reflects medical outcomes, and sometimes practice dynamics as simple as keeping doctors' appointments and participating in preventive medicine. Many reports in this journal and others provide information on this changing landscape, identifying potential barriers, charting the pace of progress, and sometimes providing meaningful solutions. “Looks like them” goes both ways of course. We all look for role models and are inspired in a special way when we see someone who had a path with which we can identify. That is why promotion of gender and racial diversity in our leaders, as well as internationalism, are powerful tools that first help people become interested in neurosurgery and then realize that they can and do belong. So how does this pertain to a medical journal? First, we promote diversity in our editorial board across many domains to be representative. This includes internationalism, gender, age, and racial participation. Our Resident Publications Fellows are but one example of this; our current Fellows Drs Ali Alawieh and Alexandra Giantini Larsen participate in and lead article peer-review, attend biweekly staff meetings, and work on unique projects. We attract and publish articles on topics related to diversity because it pertains to becoming and being a neurosurgeon. As noted above, this can affect clinical outcomes. Many focus on understanding the neurosurgical work force, both in training and in practice. Some submissions are rather straightforward and present data from different sources on the current state of affairs. But much of this information is known and appreciated, and some do not provide new insights. The best articles, like any scientific report, ask an interesting question, collect meaningful and robust data, provide useful recommendations, and bring something new to the reader. These last 2 elements are typically lacking from articles that do not get accepted. Fundamentally, we all succeed when we foster a sense of belonging, and the yield is high. In 1 instance, our ideas belong when they are accepted for publication in a major journal. That was personally gratifying for me and a major driver in my career development. Douglas Kondziolka, MD, MSc Editor-in-Chief, Neurosurgery Publications New York, New York, USA
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 | 0,005 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,013 | 0,006 |
| Communication savante | 0,013 | 0,011 |
| Science ouverte | 0,002 | 0,014 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,165 | 0,065 |
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 ».