Evidence map›Paper›PMID 38698890›Full record

ArticleFrontiers in digital health2024

Designing, implementing and testing an intervention of affective intelligent agents in nursing virtual reality teaching simulations-a qualitative study.

Michael Loizou, Sylvester Arnab, Petros Lameras, Thomas Hartley, Fernando Loizides, Praveen Kumar, Dana Sumilo

Abstract read
In one paragraph

Article in Frontiers in digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Michael LoizouCentre for Health Technology, University of Plymouth, Plymouth, United Kingdom.
Sylvester ArnabCentre for Postdigital Cultures, Coventry University, Coventry, United Kingdom.
Petros LamerasCentre for Postdigital Cultures, Coventry University, Coventry, United Kingdom.
Thomas HartleyDepartment of Computer Science, University of Wolverhampton, Wolverhampton, United Kingdom.
Fernando LoizidesSchool of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom.
Praveen KumarSchool of Health and Social Wellbeing, University of the West of England, Bristol, United Kingdom.
Dana SumiloWarwick Medical School, University of Warwick, Warwick, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Emotions play an important role in human-computer interaction, but there is limited research on affective and emotional virtual agent design in the area of teaching simulations for healthcare provision. The purpose of this work is twofold: firstly, to describe the process for designing affective intelligent agents that are engaged in automated communications such as person to computer conversations, and secondly to test a bespoke prototype digital intervention which implements such agents. The presented study tests two distinct virtual learning environments, one of which was enhanced with affective virtual patients, with nine 3rd year nursing students specialising in mental health, during their professional practice stage. All (100%) of the participants reported that, when using the enhanced scenario, they experienced a more realistic representation of carer/patient interaction; better recognition of the patients' feelings; recognition and assessment of emotions; a better realisation of how feelings can affect patients' emotional state and how they could better empathise with the patients.

Indexed as

affectiveintelligentnursingrealitysimulationteachingvirtualVR

Identifiers

PMID38698890
PMCPMC11063316

What OpenQuestion holds

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LicenceCC BY
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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.