Evidence map›Paper›PMID 39210293›Full record

ArticleBMC public health2024

Exploring perceived learning effectiveness in virtual reality health communication through the lens of construal level theory.

Chi-Horng Liao

Abstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

1 author.

Chi-Horng LiaoBachelor Program in Digital Media and Technology, Tzu Chi University, Hualien, Taiwan. lchjerry@mail.tcu.edu.tw.ORCID 0000-0002-1531-4817

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVirtual reality is increasingly being used for health communication. This study aimed to propose and test an integrated model of the determinants of perceived learning effectiveness in virtual reality (VR) within health communication. It proposes that psychological distance negatively affects immersion, flow, and presence, positively affecting perceived learning effectiveness.

methodsThe Fuzzy Decision Making Trial and Evaluation Laboratory Method (F-DEMATEL) and structural equation modeling (SEM) were used to test the proposed model. Data for the F-DEMATEL study were collected from 20 participants, whereas data for the SEM study were collected from 1104 participants, with 775 included in the final analysis.

resultsThe results of the F-DEMATEL study revealed that the three dimensions of psychological distance, emotional distance, spatial distance, and social distance are causal factors. In contrast, temporal, technical, and hypothetical distance are effect factors. The SEM results confirmed the negative effects of psychological distance on flow and presence and the positive effects of immersion and presence on perceived learning effectiveness. In addition, the mediating role of presence was confirmed.

conclusionsThe results suggest that interrelationships among the factors can enhance the perceived learning effectiveness of health communication from VR. The crucial role of ensuring low psychological distance and high engagement in VR communication is also confirmed, providing crucial implications for VR communication practitioners.

Indexed as

Health CommunicationVirtual RealityAdolescentAdultFemaleHumansLatent Class AnalysisLearningMalePsychological DistanceYoung AdultConstrual Level TheoryEngagementFuzzy Decision Making Trial and Evaluation Laboratory Method (F-DEMATEL)Health CommunicationPerceived Learning EffectivenessPsychological DistanceStructural Equation Modeling (SEM)Virtual Reality (VR)

Identifiers

PMID39210293
PMCPMC11360774

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Registered trials

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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.