COVID-19 Pandemic in University Hospital: Impact on Medical Training of Medical Interns

  1. WeiHonn Lim
  2. Li Ying Teoh
  3. Kanesh Kumaran A/L Seevalingam
  4. Shanggar Kuppusamy

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Abstract

Introduction

Coronavirus 2019 (COVID-19) has strike all nations hard since the end of year 2019, Malaysia unable to escape the fate as well. Healthcare system, financial growth, industrial development and educational programme are stunted. Inevitably, professional training and education are affected which include the medical training of medical interns.

Methods

This is a cross-sectional, pilot study to determine the impact of the pandemic on University Malaya Medical Centre (UMMC) medical interns. A survey which comprises 37-items was used. Data are analysed by Ordinal Logistic Regression Analysis.

Results

Medical interns feel that they lack clinical skills (p = 0.005) and need more exposure in surgical operations (p =0.029). Some are satisfied with the introduction of triage (p = 0.024), online teaching (p = 0.005) and bedside teaching (p=0.023). Most of them think they are fit and ready to handle the pandemic (p = 0.012 and 0.025 respectively) except first year medical interns (p = 0.029). Some feel like their time are wasted (p <0.05) as they are involved in many non-clinical activities (p = 0.003).

Conclusion

In summary, COVID-19 has a great impact on medical training amongst medical interns. Alternative measures should be taken to minimize the interruption in training of our future leaders in medical field.

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    SciScore for 10.1101/2020.10.01.20204255: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Subsequently, all data are downloaded into Excel form and transcript into SPSS Version 16.0.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.10.01.20204255v1 on medRxiv