Evidence map›Paper›PMID 36505624›Full record

ArticleJournal of family medicine and primary care2022

Development and implementation of a sustainable COVID-19 training package for healthcare workers: Experience from a teaching hospital of North India.

Amir Maroof Khan, Somdatta Patra, Anil K Jain, S V Madhu, Ashok Saxena, Anju Aggarwal, Amita Suneja, Asha Tyagi, Rajnish Avasthi, Narendra P Singh and 6 more

Open access · goldAbstract read
In one paragraph

Article in Journal of family medicine and primary care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

16 authors at 2 institutions in 1 country.

Amir Maroof KhanDepartment of Community Medicine, Medical Education Unit, University College of Medical Sciences and GTB Hospital, Delhi, India.
Somdatta PatraDepartment of Community Medicine, University College of Medical Sciences and GTB Hospital, Delhi, India.
Anil K JainDepartment of Orthopaedics, University College of Medical Sciences and GTB Hospital, Delhi, India.
S V MadhuDepartment of Endocrinology, University College of Medical Sciences and GTB Hospital, Delhi, India.
Ashok SaxenaDepartment of Anaesthesiology, University College of Medical Sciences and GTB Hospital, Delhi, India.
Anju AggarwalDepartment of Pediatrics, University College of Medical Sciences and GTB Hospital, Delhi, India.
Amita SunejaDepartment of Gynaecology and Obstetrics, University College of Medical Sciences and GTB Hospital, Delhi, India.
Asha TyagiDepartment of Anaesthesiology, University College of Medical Sciences and GTB Hospital, Delhi, India.
Rajnish AvasthiDepartment of Medicine, University College of Medical Sciences and GTB Hospital, Delhi, India.
Narendra P SinghDepartment of Microbiology, University College of Medical Sciences and GTB Hospital, Delhi, India.
Dheeraj ShahDepartment of Pediatrics, University College of Medical Sciences and GTB Hospital, Delhi, India.
Anjana DhallFlorence Nightingale College of Nursing, Guru Teg Bahadur Hospital, Delhi, India.
Rajesh KalraDepartment of Additional Medical Superintendent, GTB Hospital and UCMS, Delhi, India.
Rajesh AroraDepartment of Orthopaedics, University College of Medical Sciences and GTB Hospital, Delhi, India.
Sanjay GuptaDepartment of Surgery, University College of Medical Sciences and GTB Hospital, Delhi, India.
Neelam VaneyDepartment of Physiology, University College of Medical Sciences and GTB Hospital, Delhi, India.
Guru Teg Bahadur Hospital · INUniversity College of Medical Sciences · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: With the impending threat of future COVID-19 waves, it is imperative that teaching hospitals develop, implement, and evaluate a systematic training program to render HCW elastic in delivering COVID-19 related services. We present our experience in developing, implementing, and evaluating a sustainable and scalable COVID-19 patient management training package for healthcare workers. Materials and Methods: A mixed-methods study design was used. Rapid assessment to understand the need of the trainees and identify the available resources was done followed by planning of the training module and its implementation. The program was evaluated for effectiveness and sustainability. Data analysis was done using descriptive statistics and qualitative data generated from open-ended questions in the feedback forms and the discussions were analyzed using rapid content analysis. Results: A total of 66.8% of the doctors and 18.9% of the nurses were trained by online synchronous mode while 55.0% of the nursing officers and 47.1% of the nursing orderlies and paramedical staff were trained in onsite skill development sessions. Need assessment identified that healthcare workers were ill-prepared to use medical devices such as Bipap machines, ventilators, and oxygen delivery devices. The participants mentioned that the multidisciplinary approach and video-based demonstrations facilitated their online learning while the incremental learning approach, easy-to-understand terminology and hands-on experience facilitated their onsite skill development sessions. Conclusion: The COVID-19 training package developed was multidisciplinary, effective, sustainable, and scalable in a resource-limited setting. We suggest that this model can be adapted by healthcare organizations to develop and implement such training packages for their healthcare workers.

Indexed as

COVID-19health personnelpatient caretraining activities

Identifiers

PMID36505624
PMCPMC9730942
OpenAlexW4311287048

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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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.