A novel 5E training model and other methods implemented for rapid readiness at an ad-hoc COVID-19 facility in India

  1. Suhail Singh
  2. Anchit Raj Singh
  3. Basant Pathak
  4. TVSVGK Tilak
  5. Aparajita Kumar
  6. IPS Bhatia
  7. Jayraj Hasvi

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Abstract

Background

A ad-hoc dedicated COVID-19 hospital was setup in New Delhi, India over a span of 12 days. At this time, new teaching modalities were employed to train the staff. This study aims to identify and quantify the effectiveness of these teaching models in terms of learning which was evaluated using case scenarios before and after the teaching session.

Objectives

To assess education methods and models for training of healthcare workers during rapid deployment at an ad-hoc dedicated COVID-19 hospital.

Methods

The 5E (Engage, Explore, Explain, Elaborate and Evaluate) teaching modality through peer group teaching methods was utilised in the situation. Statistical analysis was done using Mann Whitney U test.

Results

A total of 86 participants (43 doctors and 43 nurses) answered the pre and post test questionnaires. The number of correct responses per question in the pre test (Mean + SD;49.0 + 18.53) vs post test (Mean + SD;54.40 + 15.95) with stratification by the domain of learning was analysed. No significant difference was found in the pre and post test responses.

Conclusion

Further studies of this nature will contribute towards assessing efficacy of teaching modalities employed in rapid readiness for future pandemic scenarios.

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    SciScore for 10.1101/2021.02.23.21251976: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIACUC: Clearance from the Institutional Ethics Committee was taken.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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 10.1101/2021.02.23.21251976v1 on medRxiv