Evidence map›Paper›PMID 39553706›Full record

ArticlePeerJ2024

Developing a quality indicator system for evaluating internet plus home care nursing services based on the SERVQUAL model: a Delphi-analytic hierarchy process study.

Lei Ye, Shulan Yang, Biyan Jiang, Caixia Liu, Xiaoqing Jin, Polun Chang

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Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

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6citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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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

6 authors.

Lei YeDepartment of Nursing, Zhejiang Hospital, Hangzhou, China.
Shulan YangDepartment of Nursing, Zhejiang Hospital, Hangzhou, China.
Biyan JiangDepartment of Nursing, Zhejiang Hospital, Hangzhou, China.
Caixia LiuDepartment of Nursing, Zhejiang Hospital, Hangzhou, China.
Xiaoqing JinDepartment of Acupuncture, Zhejiang Hospital, Hangzhou, China.
Polun ChangInstitute of Biomedical Informatics, Yang-Ming Chiao-Tung University, Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In the context of global population aging and the rapid development of information technology, the demand for Internet Plus Home Care Nursing (Internet+ HCN) services have been on the rise, especially in China. Internet+ HCN services have the potential to maximize existing human resources to counter the shortage of medical healthcare services. However, at present, Internet+ HCN services are difficult to scale due to the lack of standardized service quality governance. Quality indicators for service evaluation of Internet+ HCN services are under-defined. Objective: To develop a quality indicator system for evaluating Internet+ HCN services, and to shed theoretical light on assessing mHealth service quality from a user experience perspective. Methods: An initial quality indicator system was established based on scenarios related to Internet+ HCN services. The Delphi Method was applied to modify the indicators according to experts' opinions, and the analytic hierarchy process (AHP) was applied to calculate the indicator weight. Finally, a quality indicator system for evaluating Internet+ HCN services was developed based on the SERVQUAL model. Results: Altogether, 17 experts from relevant fields such as nursing education, clinical nursing, health management, and health informatics were consulted through email surveys. The response rates in both rounds of Delphi and the AHP were 100%. The average expert authority coefficients were 0.912 and 0.925 respectively in the two rounds of Delphi. Kendall's W, indicating variation coefficients, ranged from 0.262 to 0.265. Finally, a quality indicator system for evaluating Internet+ HCN services, comprising five primary indicators and 15 secondary indicators, was developed. Primary indicators and their AHP generated the following weights: assurance (0.245), reliability (0.240), tangibles (0.192), responsiveness (0.190), and empathy (0.132). Conclusions: By measuring the services quality gap between user expectations and perceptions, the proposed SERVQUAL model-based quality indicator system shows potential in improving the quality of Internet+ HCN services through the perspective of user experiences.

Indexed as

Delphi TechniqueHome Care ServicesInternetQuality Indicators, Health CareChinaHumansTelemedicineHome care nursingInternet plusService qualitySERVQUAL model

Identifiers

PMID39553706
PMCPMC11566512

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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.