The Impact of Mentoring Our Future Leaders: 12 Years of the Astct Clinical Research Training Course
Notice bibliographique
Résumé
The ASTCT Clinical Research Training Course (CRTC) is designed to build physician capacity and retain trainees/junior faculty in academic cell therapy careers. Participation is on a competitive basis. Applicants submit a clinical study proposal, career plan, CV and mentor letter of support. Each year 10-12 scholars join ∼10 senior faculty including statistician(s) for 5 days of lectures, small group work developing their protocols, career guidance and mentoring. We reviewed the course to evaluate whether it was meeting its mission. Scholars were invited to participate in an online survey and submit their cv. Scholars were asked to rate the impact of the course on their career. Data was extracted from CVs to measure academic productivity.Results: 107/146 (73%) of the scholars responded: 54% female, 59% Caucasian, 57% trainees and 61% trained in adult hematology (Table1). Responses to questions regarding the impact of the course on career choice and professional development indicated a strong positive impact of the course on scholars (Table 2). Current employment, participation in scholarly activities and productivity of former scholars demonstrated engagement in clinical research (65% of scholars >25% FTE in research), research in cellular therapy (89%), peer review (75%), and other academic activities. While scholars from the earlier cohort (2007-2012) had numerically more grants and publications and more senior academic appointments than the early cohort (2013-18), both cohorts were active in all productivity areas (Table 3).Conclusion: The ASTCT CRTC has positively contributed to retention of trainees and junior faculty in academic cellular therapy careers. The ASTCT should continue to support the CRTC and consider a second course to expand the opportunity to a larger number of scholars. The ASTCT Clinical Research Training Course (CRTC) is designed to build physician capacity and retain trainees/junior faculty in academic cell therapy careers. Participation is on a competitive basis. Applicants submit a clinical study proposal, career plan, CV and mentor letter of support. Each year 10-12 scholars join ∼10 senior faculty including statistician(s) for 5 days of lectures, small group work developing their protocols, career guidance and mentoring. We reviewed the course to evaluate whether it was meeting its mission. Scholars were invited to participate in an online survey and submit their cv. Scholars were asked to rate the impact of the course on their career. Data was extracted from CVs to measure academic productivity. Results: 107/146 (73%) of the scholars responded: 54% female, 59% Caucasian, 57% trainees and 61% trained in adult hematology (Table1). Responses to questions regarding the impact of the course on career choice and professional development indicated a strong positive impact of the course on scholars (Table 2). Current employment, participation in scholarly activities and productivity of former scholars demonstrated engagement in clinical research (65% of scholars >25% FTE in research), research in cellular therapy (89%), peer review (75%), and other academic activities. While scholars from the earlier cohort (2007-2012) had numerically more grants and publications and more senior academic appointments than the early cohort (2013-18), both cohorts were active in all productivity areas (Table 3). Conclusion: The ASTCT CRTC has positively contributed to retention of trainees and junior faculty in academic cellular therapy careers. The ASTCT should continue to support the CRTC and consider a second course to expand the opportunity to a larger number of scholars. Tables 1, 2 and 3.Table 1Demographics of Scholars at Time of CRTCn=107 %GenderFemale54RaceCaucasian59Asian23Black5Hispanic4Other6Declined3Position at CourseTrainee44Junior Faculty56TrainingAdult heme/Onc61Paeds Heme/Onc39 Open table in a new tab Table 2Impact of CRTC on Collaboration and Career PathN=107 % Agree/Strongly AgreeCRTC facilitatedCollaborations with CRTC faculty66Collaborations with co-scholars58Development as independent researcher88CRTC contributed favorably to my career choice95CRTC was instrumental in keeping me inTransplant and/or cellular therapy66Academia88Research79 Open table in a new tab Table 3Career and Academic Productivityn=107 %n=107 %Current CareerCurrent RankAcademia91Prof1Government3Assoc Prof23Industry4Asst Prof59Private practice2Lecturer8NA9Journal Peer Reviewer76Participate in CIBMTR Research67Course Year2007-122013-18Participants7572CVs4046Grants med (range)NIH2 (0, 7)0 (0, 7)Other peer3 (0, 16)4 (0, 12)Industry0 (0, 27)0 (0, 7)Peer Publications med (range)First/Senior author5 (0, 39)4 (0, 13)Total26 (0, 118)12 (1, 41)PI Clinical Trial med (range)0 (0, 29)0 (0, 9) Open table in a new tab
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».