| Literature DB >> 31878145 |
Yuxin Peng1, Pingping Yin2, Zhaohua Deng1, Ruoxi Wang1.
Abstract
BACKGROUND: In recent years, China has witnessed a surge in medical disputes, including many widely reported violent riots, attacks, and protests in hospitals. Asymmetric information between patient and physicians is one of the most critical enablers in this phenomenon, but the Web has become the primary resource for Chinese Internet applications to learn about health information and could potentially play a role in this pathway to patient-physician interaction and patient-physician trust. While considerable attention has been paid in some countries, there are few researches about China's situation for this issue. The purpose of this quantitative study was to examine the influence of online health information and the online guidance of doctors in patient health information literacy on patient-physician interaction and patient-physician trust in China.Entities:
Keywords: online health information; patient–physician interaction; patient–physician trust; perceived usefulness
Mesh:
Year: 2019 PMID: 31878145 PMCID: PMC6981828 DOI: 10.3390/ijerph17010139
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1Research model.
Demographic information.
| Characteristics | Number | Percentage | |
|---|---|---|---|
| Gender | |||
| male | 222 | 49.5% | |
| female | 224 | 50.2% | |
| Age | |||
| ≤20 | 18 | 4.0% | |
| 20–30 | 288 | 64.6% | |
| ≥30 | 140 | 31.3% | |
| Self-reported health condition | |||
| poor | 304 | 68.2% | |
| good | 142 | 31.8% | |
| intensify health consciousness | |||
| very helpful | 110 | 24.7% | |
| helpful | 308 | 69.1% | |
| no help | 28 | 6.3% | |
| online health information seeking | |||
| before visit | 114 | 25.6% | |
| after visit | 67 | 15.0% | |
| both | 225 | 50.2% | |
| others | 40 | 9.0% | |
| source of health information | |||
| medical professional | 173 | 38.8% | |
| online information | 224 | 50.2% | |
| journal | 11 | 2.5% | |
| friends | 31 | 7.0% | |
| others | 7 | 1.6% | |
| Issues that should be improved | |||
| patient–physician communication | 174 | 39.0% | |
| patient–physician trust | 166 | 37.2% | |
| service attitude of medical professional | 98 | 22.0% | |
| others | 8 | 1.8% | |
Variable measurement and source.
| Potential Variables | Items | Questions | Source |
|---|---|---|---|
| Perceived usefulness of online health information | 1 | Online health information helps me validate doctors’ diagnosis | Tan S S et al. |
| 2 | Online health information helps me realize my own health status | ||
| Perceived usefulness of online doctors’ service | 1 | I receive caring and helping from doctors on the online health community. | Iverson S A et al. |
| 2 | Online doctors will offer me necessary information when I face disease-related problems. | ||
| Patient–physician interaction | 1 | Interacting with doctors makes me feel smooth. | Oh, HJ et al. [ |
| 2 | I will try to get practical information from doctors for my disease management. | ||
| 3 | During my interaction with doctors, the doctors demonstrate sufficient devotion to the management of my problems. | ||
| 4 | When I find some information regarding my disease, I will bring them to my doctor. | ||
| Patient–physician trust | 1 | I trust the doctors so much I always try to follow his/her advice. | Anderson L A et al. |
| 2 | My doctor is a real expert in taking care of medical problems like mine. | ||
| 3 | If my doctor tells me something is so, then it must be true. | ||
| 4 | I trust my doctor to put my medical needs above all other considerations when treating my medical problems. |
Item loadings and validities.
| Construct | Items | Factor Loadings | Composite Reliability | Average Variance Extracted | Cronbach’s |
|---|---|---|---|---|---|
| Patient–Physician Interaction | INT1 | 0.715 | 0.846 | 0.579 | 0.853 |
| INT2 | 0.735 | ||||
| INT3 | 0.772 | ||||
| INT4 | 0.818 | ||||
| Patient–Physician Trust | TRU1 | 0.772 | 0.8088 | 0.5153 | 0.844 |
| TRU2 | 0.758 | ||||
| TRU3 | 0.686 | ||||
| TRU4 | 0.648 | ||||
| perceived usefulness of online health information | UI1 | 0.773 | 0.6985 | 0.5357 | 0.688 |
| UI2 | 0.691 | ||||
| perceived usefulness of online doctors’ services | PH1 | 0.848 | 0.8326 | 0.7132 | 0.779 |
| PH2 | 0.841 |
Correlation coefficient matrix and square roots of the average variance extracted (AVE) (shown as diagonal elements).
| . | PUI | PUS | INT | TRU |
|---|---|---|---|---|
| PUI | 0.732 | |||
| PUS | 0.403 | 0.845 | ||
| INT | 0.610 | 0.526 | 0.761 | |
| TRU | 0.633 | 0.455 | 0.662 | 0.718 |
Note: the square roots of AVE are in boldface. Abbreviations: PUI = perceived usefulness of information, PUS = perceived usefulness of service, INT = patient–physician interaction, TRU = patient–physician trust.
Figure 2Analysis results of structural model. (*** Statistically significant at the 1% level; ** Statistically significant at the 5% level; * Statistically significant at the 10% level, two-sided test).