Evidence map›Paper›PMID 41333782›Full record

ArticleFrontiers in medicine2025

Mechanism of career resilience formation during the role transition of medical interns: a grounded theory study.

Jinyu Zhou, Lifu Jin, Yamin Hu

Abstract read
In one paragraph

Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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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.

Jinyu ZhouSchool of Management, Jiangsu University, Zhenjiang, Jiangsu, China.
Lifu JinSchool of Management, Jiangsu University, Zhenjiang, Jiangsu, China.
Yamin HuSchool of Management, Jiangsu University, Zhenjiang, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The complexity, difficulty and uncertainty inherent in the medical field pose significant challenges to physicians' ability to adapt. For interns, career resilience plays a crucial role in the transition from academia to clinical practice. This study aimed to explore the mechanism of career resilience formation during the role-transition process of medical interns. Methods: We conducted in-depth semi-structured interviews with 16 medical interns from a regional medical university in Northwest China, recruited via purposive and snowball sampling through professional networks. Data were collected between August and September 2024; interviews lasted 35-50 min (online/offline), were audio-recorded, transcribed verbatim, and analyzed using Straussian grounded theory (open-axial-selective coding with constant comparison), guided by the Critical Incident Technique and the STAR framework. The research team performed the coding, and the process was validated through regular peer-debriefing sessions with two independent researchers. Analysis was facilitated using NVivo software (version 12) until theoretical saturation was reached. Results: A Challenge-Resource-Adaptation (CRA) model was constructed to explain how medical interns develop career resilience in the face of practical challenges and role transitions during the internship. Three pillars of career resilience were identified: (1) career development challenges (knowledge updating, identity establishment, career planning); (2) facilitative resources (guidance and feedback, resources and opportunities, emotional support); and (3) proactive career adaptation (compliance/pressure management and innovative breakthrough). The CRA model depicts a recursive loop whereby each innovative breakthrough elevates demands for further knowledge updating. Illustratively, weekly mentor-guided guideline reviews and skills-lab rehearsal closed knowledge-practice gaps, enabling independent ward rounds; exposure to advanced procedures helped clarify specialty choices. Conclusion: Career development challenges are the trigger factor, facilitative resources are the favorable conditions, and career adaptation behavior is the external manifestation of resilience. Overall, career resilience during internship emerges as a self-reinforcing process in which targeted resources convert concrete challenges into adaptive behaviors. The CRA model identifies mentor feedback, advanced clinical opportunities, and emotional support as key leverage points for strengthening medical curricula and workplace design.

Indexed as

career resiliencegrounded theoryinternsproactive career adaptationrole transition

Identifiers

PMID41333782
PMCPMC12665529

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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.