Evidence map›Paper›PMID 41257142›Full record

ArticleCureus2025

Improving New Doctors' Confidence of Starting Work Through Simulation-Based Training.

Janhvi Shah, Niraj C Doshi, Akash Doshi

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Janhvi ShahOral and Maxillofacial Surgery, Barts Health NHS Trust, London, GBR.
Niraj C DoshiMedicine, University College London, London, GBR.
Akash DoshiMedicine, Barts Health NHS Trust, London, GBR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction The transition from final year medical student to Foundation Year 1 (FY1) doctor is widely recognised as challenging, with many new FY1s reporting a lack of preparedness. This contributes to increased stress, higher rates of burnout and potential patient safety issues. Although a mandatory shadowing period has been introduced, the structure and content vary greatly and survey data demonstrates FY1s still feel unprepared. Methods A free one-day simulation-based teaching course was developed and refined over seven years using an action research model. The course aimed to increase confidence prior to starting FY1. Session content was developed and refined over these years and includes commonly encountered clinical and non-clinical skills. Pre- and post-course surveys measured confidence at starting FY1. Demographic and qualitative feedback was also gathered. Results Between 2017 and 2024, a total of 1,406 final year medical students attended the course. There were increasing numbers of international medical students (IMGs) who attended. The pre- and post-course average confidence rating was 1.99 and 3.68, respectively, out of a five-point Likert scale (with 1 being not at all confident and 5 being completely confident). Common themes for improvement from qualitative feedback identified changes in course content and structure. Of these changes, transitioning to a simulation-based course in 2021 led to the largest increase in confidence. Conclusion This study highlights the effectiveness of a simulation-based teaching course to improve FY1 preparedness. It can be easily replicated and localised on a wider scale for use in hospital inductions for new doctors.

Indexed as

doctors in trainingfoundation year 1medical educationnew graduatessimulation

Identifiers

PMID41257142
PMCPMC12621282

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.