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. 2022 May;8(5):e09351.
doi: 10.1016/j.heliyon.2022.e09351. Epub 2022 Apr 28.

The psychological mechanism of internet information processing for post-treatment evaluation

Quan-Hoang Vuong  1 , Tam-Tri Le  1 , Viet-Phuong La  1 , Minh-Hoang Nguyen  1
Affiliations

The psychological mechanism of internet information processing for post-treatment evaluation

Quan-Hoang Vuong et al. Heliyon. 2022 May.

Abstract

Digital healthcare has been greatly benefiting the public health system, especially during the COVID-19 pandemic. In digital healthcare, information communication through the Internet is crucial. The current study explores how patients' accessibility and trust in Internet information influence their decisions and ex-post assessment of healthcare providers by employing the Bayesian Mindsponge Framework (BMF) on a dataset of 1,459 Vietnamese patients. We find that patients' accessibility to Internet information positively affects the perceived sufficiency of information for choosing a healthcare provider, and their trust in the information intensifies this effect. Internet information accessibility is negatively associated with post-treatment assessment of healthcare providers, and trust also moderates this effect. Moreover, patients considering professional reputation important while making a decision are more likely to regard their choices as optimal, whereas patients considering services important have contradicting tendencies. Based on these findings, a concern about the risk of eroding trust toward Internet sources about healthcare information is raised. Thus, quality control and public trust-building measures need to be taken to improve the effectiveness of healthcare-related communication through the Internet and facilitate the implementation of digital healthcare.

Keywords: Digital healthcare; Healthcare quality; Internet information; Mindsponge mechanism; Public communication.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Psychological mechanism of Internet information processing.
Figure 2
Figure 2
Model 1's logical network.
Figure 3
Figure 3
Model 1's PSIS diagnostic plot.
Figure 4
Figure 4
Model 1's trace plots.
Figure 5
Figure 5
Model 1's Gelman plots.
Figure 6
Figure 6
Model 1's autocorrelation plots.
Figure 7
Figure 7
Distributions of Model 1's posterior coefficients on an interval plot.
Figure 8
Figure 8
Model 2's logical network.
Figure 9
Figure 9
Model 2's PSIS diagnostic plot.
Figure 10
Figure 10
Model 2's trace plots.
Figure 11
Figure 11
Model 2's Gelman plots.
Figure 12
Figure 12
Model 2's autocorrelation plots.
Figure 13
Figure 13
Distributions of Model 2's posterior coefficients on a density plot.
Figure 14
Figure 14
Model 3's logical network.
Figure 15
Figure 15
Model 3's PSIS diagnostic plot.
Figure 16
Figure 16
Model 3's trace plots.
Figure 17
Figure 17
Model 3's Gelman plots.
Figure 18
Figure 18
Model 3's autocorrelation plots.
Figure 19
Figure 19
Distributions of Model 3's coefficients with HPDI 95%.
See this image and copyright information in PMC

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