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可持续劳动力的说服性设计解决方案:实时农村医疗保健专业人员能力支持的说服性应用程序综述。

Persuasive Design Solutions for a Sustainable Workforce: Review of Persuasive Apps for Real-Time Capability Support for Rural Health Care Professionals.

机构信息

New South Wales Rural Doctors Network, Hamilton, Australia.

出版信息

JMIR Mhealth Uhealth. 2022 Feb 7;10(2):e33413. doi: 10.2196/33413.

DOI:10.2196/33413
PMID:35129447
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8861870/
Abstract

BACKGROUND

There is a need to further investigate how persuasive design principles can change rural health professionals' behaviors to look after their own health workforce capability. Several theories are used when developing apps to persuade people to change behavior, including the Persuasive System Design Model, consisting of primary task, dialogue, system credibility, and social support categories, and Cialdini's principles of persuasion. These have not been analyzed yet in the field of health workforce capability.

OBJECTIVE

This study aims to determine the persuasive design techniques used in capability building-related apps and to provide recommendations for designing a health workforce app to increase their persuasiveness.

METHODS

A Python script was used to extract a total of 3060 apps from Google Play. Keywords centered around health workforce capability elements. App inclusion criteria were as follows: been updated since 2019, rated by users on average 4 and above, and more than 100,000 downloads. Next, 2 experts reviewed whether 32 persuasive strategies were used in the selected apps, and these were further analyzed by capability categories: competencies and skills, health and personal qualities, values and attitudes, and work organization.

RESULTS

In all, 53 mobile apps were systematically reviewed to identify the persuasive design techniques. The most common were surface credibility (n=48, 90.6%) and liking (n=48), followed by trustworthiness (n=43, 81.1%), reminders (n=38, 71.7%), and suggestion (n=30, 56.6%). The techniques in the social support domain were the least used across the different apps analyzed for health workforce capability, whereas those in the primary task support domain were used most frequently. The recommendations reflect learnings from our analysis. These findings provided insight into mobile app design principles relevant to apps used in improving health workforce capability.

CONCLUSIONS

Our review showed that there are many persuasive design techniques that can assist in building health workforce capability. Additionally, several apps are available in the market that can assist in improving health workforce capability. There is, however, a specific lack of digital, real-time support to improve health workforce capability. Social support strategies through using social support persuasive design techniques will need to be integrated more prominently into a health workforce capability app. An app to measure and monitor health workforce capability scores can be used in conjunction with direct real-world person and real-time support to discuss and identify solutions to improve health workforce capability for rural and remote health professionals who are at high risk of burnout or leaving the rural health workforce.

摘要

背景

需要进一步研究如何运用有说服力的设计原则来改变农村卫生专业人员照顾自身卫生人力能力的行为。在开发应用程序以说服人们改变行为时,使用了几种理论,包括主要任务、对话、系统可信度和社会支持类别以及 Cialdini 的说服原则。这些在卫生人力能力领域尚未得到分析。

目的

本研究旨在确定与能力建设相关的应用程序中使用的有说服力的设计技术,并为设计一个增加其说服力的卫生人力应用程序提供建议。

方法

使用 Python 脚本从 Google Play 中总共提取了 3060 个应用程序。关键词围绕卫生人力能力要素。应用程序纳入标准如下:自 2019 年以来已更新、用户平均评分 4 分及以上、下载量超过 10 万次。然后,两名专家审查了 32 种有说服力的策略是否在选定的应用程序中使用,然后根据能力类别进一步分析这些策略:能力和技能、健康和个人素质、价值观和态度以及工作组织。

结果

总共系统地审查了 53 个移动应用程序,以确定有说服力的设计技术。最常见的是表面可信度(n=48,90.6%)和喜欢(n=48),其次是可信度(n=43,81.1%)、提醒(n=38,71.7%)和建议(n=30,56.6%)。在分析的不同应用程序中,社会支持领域的技术使用最少,而主要任务支持领域的技术使用最多。建议反映了我们分析的结果。这些发现为改进卫生人力能力的应用程序提供了有关移动应用程序设计原则的见解。

结论

我们的审查表明,有许多有说服力的设计技术可以帮助建立卫生人力能力。此外,市场上有许多可用于提高卫生人力能力的应用程序。然而,特别缺乏用于提高卫生人力能力的数字、实时支持。通过使用社会支持有说服力的设计技术,将需要更突出地整合社会支持策略,以纳入卫生人力能力应用程序。可以结合直接的现实世界人员和实时支持使用一种用于衡量和监测卫生人力能力分数的应用程序,以讨论和确定解决方案,以提高处于倦怠或离开农村卫生人力高风险的农村和偏远地区卫生专业人员的卫生人力能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f50c/8861870/aeb201d9bdc4/mhealth_v10i2e33413_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f50c/8861870/1901d59f52ef/mhealth_v10i2e33413_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f50c/8861870/4518f27ed5bd/mhealth_v10i2e33413_fig2.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f50c/8861870/2e60d4d99c53/mhealth_v10i2e33413_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f50c/8861870/879f7670ab4a/mhealth_v10i2e33413_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f50c/8861870/94aee95762cb/mhealth_v10i2e33413_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f50c/8861870/aeb201d9bdc4/mhealth_v10i2e33413_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f50c/8861870/1901d59f52ef/mhealth_v10i2e33413_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f50c/8861870/4518f27ed5bd/mhealth_v10i2e33413_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f50c/8861870/e5aab46d62ec/mhealth_v10i2e33413_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f50c/8861870/2e60d4d99c53/mhealth_v10i2e33413_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f50c/8861870/879f7670ab4a/mhealth_v10i2e33413_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f50c/8861870/94aee95762cb/mhealth_v10i2e33413_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f50c/8861870/aeb201d9bdc4/mhealth_v10i2e33413_fig7.jpg

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