Evidence map›Paper›PMID 38192552›Full record

ArticleFrontiers in public health2023

Public first aid education model design study based on user experience.

Jing Luo, Kaiqiao Zheng, Wudi Hong

Abstract read
In one paragraph

Article in Frontiers in public health, 2023. 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.

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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

3 authors.

Jing LuoCollege of Art and Design, Division of Arts, Shenzhen University, Shenzhen, Guangdong, China.
Kaiqiao ZhengCollege of Art and Design, Division of Arts, Shenzhen University, Shenzhen, Guangdong, China.
Wudi HongCollege of Art and Design, Division of Arts, Shenzhen University, Shenzhen, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Presently, China's first aid penetration rate remains relatively low, leaving ample room for improvement in the existing first aid education model. Given its role as a service for the general public, public first aid education must thoroughly consider the learning needs and experiences of the public when designing the teaching mode. Methods: Semi-structured interviews were employed to gather detailed insights into participants' experiences in the first aid learning process. Subsequently, NVivo was utilized to analyze the interviews and identify specific design strategies. Additionally, a 7-point scale questionnaire was employed to assess the intervention effects of music familiarity and the simulation of teaching aids on users' willingness and confidence in learning. Building upon the design strategy, a "feedback device + app" approach was proposed. Finally, user satisfaction was evaluated through a scale questionnaire. Results: The use of familiar music had a significant positive effect on participants' willingness and confidence to learn, while users' fear of teaching aids had no effect on willingness and confidence. The user experience-based first aid education model can better meet the public's learning needs for first aid knowledge and skills. Conclusion: This study proposes a first aid education model based on user experience design methodology, which optimizes the public's self-learning experience by evoking positive emotions while circumventing negative emotions. The educational model was recognized by users in terms of design concepts and is expected to help increase first aid prevalence in the future.

Indexed as

First AidLearningComputer SimulationFearHumansModels, EducationalCPRemotionfirst aidpublic educational modeluser experience

Identifiers

PMID38192552
PMCPMC10773782

What OpenQuestion holds

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LicenceCC BY
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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.