The Influence of Human Connections and Collaboration on Research Grant Success at Various Career Stages: Regression Analysis
Notice bibliographique
Résumé
BACKGROUND: Documenting the grant acquisition characteristics of a highly selective group of researchers could provide insights into the research and faculty development of talented individuals, and the insights gained to foster such researchers will help university management strengthen their research capacity. OBJECTIVE: This study examines the role of human connections in the success of biomedical researchers in Japanese universities. METHODS: This study used grant data from the Grants-in-Aid for Scientific Research (GIA) program, the largest competitive research funding program in Japan, to collect information on projects and their implementation systems obtained throughout the participants' careers. Grant success was measured by the number and amounts of the awards obtained while participants occupied the role of principal investigator. Human connections were quantified by the number of projects in which the participants took part as members and were classified by their relationship with the project leader. Data were matched with information on career history, publication performance, and experience of the participants with government-funded programs apart from GIA and were analyzed using univariate and multivariate regression analyses. RESULTS: Early-career interpersonal relationships, as measured using the h-index value of the researchers who provided the participants with their initial experience as project members, had a positive effect on grant success. The experience of contributing to prestigious research programs led by top researchers dramatically increased the cumulative amount of GIA awards received by the participants over time. Univariate logistic regression analyses revealed that more interactions with upper-level researchers resulted in fewer acquisitions of large programs (odds ratio [OR] 0.67, 95% CI 0.50-0.89). Collaboration with peers increased the success rate of ≥2 research grants in large programs in situations in which both the participant and project leader were professors (OR 1.16, 95% CI 1.06-1.26). Tracking the process of research development, we found that collaboration during the periods of 10 to 14 years and 15 to 19 years after completing a doctorate degree determined the size of the project that the participant would obtain-interactions with peer researchers and subordinates during the 10- to 14-year postdegree period had positive effects on ≥2 large-program acquisitions (OR 1.51, 95% CI 1.09-2.09 and OR 1.31, 95% CI 1.10-1.57, respectively), whereas interactions with subordinates during the 15- to 19-year postdegree period also had positive effects (OR 1.25, 95% CI 1.25-1.07). Furthermore, relationships that remained narrowly focused resulted in limited grant success for small programs. CONCLUSIONS: Human networking is important for improving an individual's ability to obtain external funding. The results emphasize the importance of having a high-h-indexed collaborator to obtain quality information early in one's career; working with diverse, nonsupervisory personnel at the midcareer stage; and engaging in synergistic collaborations upon establishing a research area in which one can take more initiatives.
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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 | 0,017 | 0,067 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».