Usage Information
Abstract
Effective grant writing is an essential skill for physician-scientists to achieve academic independence and long-term career success. Previous studies have established that receiving an NIH F30 or F31 during predoctoral training is correlated with success in subsequent training stages and contributes to the retention of physician-scientists in academia. However, many trainees experience challenges in predoctoral grant writing that prevent them from submitting a grant or developing a well-rounded application. Identifying and addressing these challenges remains crucial; however, limitations in NIH public reporting exclude data on prospective applicants and applicants who were not awarded grants. In this study, we employed a national survey of trainees to identify perceived needs and barriers to grant writing as well as factors associated with NIH predoctoral grant funding success. We found that limited mentor and sponsor support to developing quality applications, constrained eligibility timelines, and limited available awards were prominent barriers to submission, while access to previously funded applications was the most valued resource among respondents. Using these findings, we highlight opportunities for interventions at the federal, institutional, applicant, and medical and scientific society levels to improve predoctoral grant writing feasibility and success.
Authors
Brian J. Thomas, Tiger S. Zhang, Daniel C. Brock, Timothy J. Ley, William D. Arnold, Cynthia Y. Tang
Usage data is cumulative from September 2026 through September 2026.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 134 | 0 |
| 51 | 0 | |
| Supplemental data | 21 | 0 |
| Citation downloads | 39 | 0 |
| Totals | 245 | 0 |
| Total Views | 245 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.
