Evidence map›Paper›PMID 38084198›Full record

ArticleCureus2023

Video-Based Simulation Among Saudi Undergraduate Nursing Students During COVID-19: A Qualitative Study.

Yasir S Alsalamah, Maryam S Alharbi, Safiah Labani, Reham A Abed, Awadh S Al Harbi, Esin Kavuran, Nihan Türkoğlu, Hanan Al-Nuqaidan, Mirna Fawaz

Abstract read
In one paragraph

Article in Cureus, 2023. 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

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

2 citing papers in PubMed.

  1. Article
  2. Article
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

9 authors.

Yasir S AlsalamahDepartment of Nursing, Mental Health Hospital, Qassim Health Cluster, Qassim, SAU.
Maryam S AlharbiDepartment of Pediatric Oncology & Hematology, King Abdullah Specialized Children's Hospital, Riyadh, SAU.
Safiah LabaniResearch Unit, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, SAU.
Reham A AbedMedical and Surgical Ward, King Saud Medical City, Riyadh, SAU.
Awadh S Al HarbiDepartment of Nursing, King Saud Hospital, Riyadh, SAU.
Esin KavuranDepartment of Nursing, Ataturk University, Erzurum, TUR.
Nihan TürkoğluDepartment of Public Health Nursing, Ataturk University, Erzurum, TUR.
Hanan Al-NuqaidanDepartment of Nursing, Ministry of Health, Kuwait, KWT.
Mirna FawazDepartment of Nursing, Beirut Arab University, Beirut, LBN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Because of university closures due to COVID-19 confinement, video-based simulation, a training technique based on high-fidelity simulations, was introduced in reaction to the need to adapt high-fidelity clinical simulation experiences to digital platforms. Purpose This study aims to evaluate the perceptions of nursing students in Saudi Arabia regarding the shift from face-to-face simulation experiences to video-based simulation during the COVID-19 pandemic. Methods This study employed a phenomenological exploratory qualitative research design among 32 nursing students from various academic levels. Results The thematic analysis gave rise to five themes namely, "Enhanced Learning and satisfaction", "Improved communication skills", "Lack of hands-on experience", "More comfortable experience", and "Technical Barriers". Conclusion The students in this study have indicated that they were satisfied with video-based simulation, where they reported enhanced learning, better communication skills, and more perceived comfort, while concerns regarding technical issues and nursing skills were raised.

Indexed as

covid-19nursing educationnursing studentssimulationvideo-based learning

Identifiers

PMID38084198
PMCPMC10710736

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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.