National Institutes of Health Funding Gaps for Principal Investigators
Notice bibliographique
Résumé
Importance: Early-stage and established investigators compete for a limited supply of funds from the National Institutes of Health (NIH). Regardless of their previous funding success, many principal investigators (PIs) encounter a funding gap in which they no longer receive ongoing funding from the NIH. Objective: To determine incidence rates of PI-level funding gaps, the mean funding gap length, and whether these 2 metrics are associated with previous funding success. Design, Setting, and Participants: This study was conducted using data from NIH RePORTER. Historical datafiles for fiscal year (FY) 2011 to FY 2021 were aggregated to generate 2 master datafiles for this period: all NIH awards and only R01 awards. PIs with no funding in FY 2011 or FY 2021 were removed. PIs were sorted by FY 2011 total funding amounts and grouped by quarter of amount. Results: A total of 39 944 unique researchers were awarded 220 131 NIH awards, of which 103 753 were R01 awards. For all NIH awards, there was an overall linear increase from top quarter to bottom quarter in the percentage of PIs who had at least 1 year without funding (from 27% to 75%), percentage of these gap PIs who had at least 2 consecutive years without funding (from 56% to 68%), and mean maximum consecutive years without funding for gap PIs (2.2 years to 3.1 years). For only R01 awards, there was an overall linear increase from top quarter to bottom quarter in the percentage of PIs who had at least 1 year without funding (50% to 74%), percentage of gap PIs who had at least 2 consecutive years without funding (59% to 71%), and mean maximum consecutive years without funding for gap PIs (2.4 years to 3.1 years). Conclusions and Relevance: In this cohort study of NIH-funded investigators, PIs with higher NIH funding were less likely to experience a funding gap. Additionally, when these PIs encountered a funding gap, this period without funding was shorter; however, among all PIs, funding gaps typically lasted 2 to 3 years. These associations were found inclusive of all NIH awards and when analysis was limited to only R01 awards. These findings may be useful to PIs and academic institutions as they prepare, structure, and project research resource allocations.
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 | 0,009 | 0,035 |
| 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 | 0,000 | 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 ».