Evidence map›Paper›PMID 38456505›Full record

ReviewJCI insight2024

Interventions to support fellowship application success among predoctoral physician-scientists.

Reiko Maki Fitzsimonds, Fred S Gorelick, Barbara I Kazmierczak

Abstract readReview
In one paragraph

Review in JCI insight, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. When Training Pipelines Break: What NIH F31 Terminations Mean for the Future of Public Health Research.Health education & behavior : the official publication of the Society for Public Health Education · 2026
    Article
  2. Article
  3. Article
  4. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Reiko Maki FitzsimondsMD-PhD Program.
Fred S GorelickMD-PhD Program.
Barbara I KazmierczakMD-PhD Program.

Funding

Medical Scientist Training ProgramT32GM136651 · NIGMS · YALE UNIVERSITY · PI BARBARA I KAZMIERCZAK · 2020 to 2026
$16.5M
BLRD VA I01 BX003250NIGMS NIH HHS T32 GM136651
6 · The paper itself

Abstract

A critical element of physician-scientist training is the development and practice of core competencies that promote success in research careers. The ability to develop compelling training and research proposals is one such foundational skill. The NIH Ruth L. Kirschstein National Research Service Award (NRSA) individual fellowship for dual-degree students (F30, F31, or F31-Diversity) creates an ideal opportunity to provide formal instruction in grant-writing skills to physician-scientists early in training. In the guided process of preparing a predoctoral fellowship application, students learn to formulate clear short- and long-term research and training goals; construct a comprehensive, well-reasoned, and rigorous proposal; become familiar with funding agency priorities; and gain strategic insights into the peer review system. Beyond building scientific writing skills, the application process for an NRSA F30 or F31 is an opportunity for trainees to strengthen mentor-mentee relationships, identify learning opportunities key to their scientific development, and build effective research and mentoring teams. These skills also apply to developing future postdoctoral mentored K applications or faculty research program grants. Here, we outline key features of the structured proposal development training developed for students in the Yale MD-PhD Program and review outcomes associated with its implementation.

Indexed as

Awards and PrizesPhysiciansFacultyFellowships and ScholarshipsHumansMentors

Identifiers

PMID38456505
PMCPMC10972582

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.