Abstract: What Factors Contribute to the Academic Productivity of Plastic Surgeons?
Notice bibliographique
Résumé
INTRODUCTION: Success in academic surgery is typically measured by the number of publications, citations, and the amount of research funding generated by an individual or department.1 Additionally, metrics of academic productivity are often used as part of the criteria for tenure or promotion across multiple specialties.2 The purpose of this study was to identify academic characteristics that distinguish plastic surgery programs with high academic output as measured by citations, publications, and NIH funding. MATERIALS AND METHODS: The American Council of Academic Plastic Surgeons (ACAPS) website was used to generate a list of all plastic surgery divisions/departments with residency programs. Scholarly metrics were determined for 955 faculty at the 88 ACGME plastic surgery departments and divisions with residency programs. The database was binned into tertiles by numbers of citations per department/division (high, H, medium, M, low, L). Characteristics were compared between these groups to identify the traits that set these programs apart. RESULTS: Median numbers of faculty per program were 9. The mean publications per department/division were 479, citations; 9984, publications per faculty; 38, citations per faculty; 742. Programs in H had higher numbers of publications even after adjusting for departmental size (H:59, M:33, L:21, p<0.05). Programs in the H group also had higher numbers of mean PhDs and MD-PhDs per division, and higher total numbers of NIH grants (H:7.5, M:1.2, L:0.1, p<0.05), and R01/P01/U01 grants (H:2.5, M:0.5, L:0, p<0.05). There were no differences in gender distribution across these groups. Programs in H had significantly more total residents H:11.9 vs. M:7.6 and L:6.1, p<0.05 which was mainly driven by higher numbers of integrated residents. CONCLUSIONS: The strongest determinants of academic productivity among plastic surgery programs appear to be effective utilization of faculty with advanced degrees, emphasis on NIH funding, and the presence of integrated residency programs. A recent study suggested that the presence of an integrated residency as well as subspecialty fellowships increases the productivity of academic faculty in plastic surgery.3 A focus on NIH funding and the incorporation of integrated residency programs may be the optimal way to increase academic productivity in plastic surgery. REFERENCES: 1. Mann M, Tendulkar A, Birger N, et al. National institutes of health funding for surgical research. Ann Surg. 2008;247:217–221. 2. Beasley BW, Wright SM, Cofrancesco J, Jr, et al. Promotion criteria for clinician-educators in the United States and Canada. A survey of promotion committee chairpersons. JAMA. 1997;278:723–728. 3. Duquette S, Valsangkar N, Sood R, et al. Do plastic surgery programs with integrated residencies or subspecialty fellowships have increased academic productivity? Plastic and Reconstructive Surgery Global Open. 2016;4(2):e614. doi:10.1097/GOX.00000000000005
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».