Evidence map›Paper›PMID 37283628›Full record

ArticleFrontiers in medicine2023

The effect of an information intervention on the career commitment of medical students: evidence from a randomized experiment.

Chunqing Li, Xiaoyang Ye, Chen Yu, Hongbin Wu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.9field-weighted citation impact, top 25% of its field
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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
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

4 authors at 2 institutions in 2 countries.

Chunqing LiInstitute of Medical Education/National Center for Health Professions Education Development, Peking University, Beijing, China.
Xiaoyang YeAnnenberg Institute for School Reform, Brown University, Providence, RI, United States.
Chen YuInstitute of Medical Education/National Center for Health Professions Education Development, Peking University, Beijing, China.
Hongbin WuInstitute of Medical Education/National Center for Health Professions Education Development, Peking University, Beijing, China.
Peking University · CNBrown University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The needs-based shortage of healthcare workers is severe worldwide and it would be exacerbated if many medical students switch to other careers after graduation. Maintaining and improving the career commitment of medical students, which could be a feasible, effective, and scalable way to reduce the attrition rate, is essential in medical education. We designed a randomized experiment to test whether an information intervention based on role modeling could enhance medical students' career commitment. Methods: In the randomized experiment, the sample ( Results: The results showed that the information intervention statistically significantly reduced medical students' dropout intention by 2.7 percentage points (95% CI: -0.037 to -0.016, Conclusion: Role model-based information intervention improves the career commitment of medical students. The underlying behavioral model is that, when students use a role model as their reference point, they consider dropout as a substantial welfare loss. Role modeling is an effective way to improve the career commitment of medical students, especially for males and senior students.

Indexed as

behavioral economicscareer commitmentdifference-in-differencesinformation interventionrole modeling

Identifiers

PMID37283628
PMCPMC10239797
OpenAlexW4377291949

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.