How Do Team-Level and Individual-Level Linguistic Styles Affect Patients’ Emotional Well-Being—Evidence from Online Doctor Teams
Abstract
:1. Introduction
2. Literature Review
2.1. Doctor–Patient Communication and Patient’s Emotional Well-Being
2.2. Text Mining in Online Consultation Platform
2.3. Research Gaps
- Previous studies have recognized patients’ emotions as an important issue in doctor–patient communication, but limited studies have used secondary data to explore patients’ emotional health in doctor–patient communication through empirical methods, especially at the team level.
- Language is the basic form of doctor–patient communication in online consultation platform, so doctors’ linguistic styles may affect the patients’ perceptions and judgments of the communication process. However, few studies have unfolded the textual interactions produced by doctor teams from the perspective of linguistic style.
3. Hypothesis Development
3.1. Vocabulary Richness and Patients’ Emotional Well-Being
3.2. Health-Related Terms and Patients’ Emotional Well-Being
3.3. Emotional Expression and Patients’ Emotional Well-Being
4. Methodology
4.1. Sample and Data Collection
4.2. Variable Measurement
4.2.1. Dependent Variable
4.2.2. Independent Variable
4.2.3. Control Variables
4.2.4. Regression Models
5. Results
5.1. Descriptive Statistical Analysis
5.2. Correlation Analysis
5.3. Regression Analysis
5.4. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Linguistic Inquiry and Word Count (LIWC)
LIWC Category | Subcategory | Examples | Representative Words in Research Testbed |
---|---|---|---|
Perceptual Words | See | Gaze, look, stare | 注视、看、瞅 |
Hear | Listen, hear, loud | 听、闻、响亮 | |
Feel | Touch, contact, warmth | 触摸、接触、温暖 | |
Biological Words | Body | Skin, gut, face | 皮肤、肠胃、脸 |
Health | Headache, cold, medicine | 头疼、感冒、药剂 | |
Sexual | Love, intimacy | 情爱、亲密 | |
Ingestion | Eating, food, dishes | 吃、食、碗筷 |
Appendix B. Chinese Sentiment Analysis Tools SnowNLP
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Inclusion Criteria | Exclusion Criteria |
---|---|
Empirical Research | Non-English studies |
Review Article | Conference Abstracts |
Health outcomes of doctor–patient communication | Not pertinent to the field of investigation |
Information and emotional support in online consultation platform |
Dimensionality | Author | Interaction Mode/Scenario | Research Content |
---|---|---|---|
Information | Peng et al. [40] | doctor-to-patient information disclosure | Aims: identify potential topics in doctors’ self-disclosure information, and explore the impact of topic diversity in doctor self-disclosure on patient choice. Methods: LDA topic model; hierarchical clustering method Results: excessive quantity of information and semantic topic diversity can raise barriers for patient’s decision. |
Park et al. [42] | patient-to-patient communication | Aims: examine how different types of supportive messages posted on OHCs encourage users to increase their health resilience. Method: directed content analysis Results: self-efficacy-oriented messages affect helpfulness, while response-efficacy-oriented messages influence the relationships among helpfulness, goal-setting, and health resilience. | |
Emotion | Lu et al. [43] | patient-to-patient communication | Aims: calculate the emotional representation of depressed patients in texts from an online consultation platform, and further investigate whether the use of online communities helps improve depression. Methods: Baidu AI’s natural language processing method Results: Emotional support positively affect the treatment of depression. |
Liu et al. [37] | patient-to-patient communication | Aims: explore various patterns of information exchange and social support in web-based health care communities and identify factors that affect such patterns. Methods: social network analysis; text mining techniques Results: polarized sentiment increases the chances of users to receive replies, and optimistic users play an important role in providing social support to the entire community. | |
Information and emotion | Chen et al. [44] | patient-to-patient communication | Aims: consider whether or not linguistic signals in posts (including sentiment valence, linguistic style matching, readability, post length, and spelling) impact the amount of support received. Methods: social support classification using SVM; structured information extraction Results: affective linguistic signals, including negative sentiment and linguistic style matching, are effective in invoking both informational and emotional support from the community. |
Jiang et al. [17] | patient-to-doctor communication | Aims: how various linguistic characteristics of patients’ communication in these communities affect their social support outcomes. Methods: linguistic analysis; exponential random graph models Results: lexical richness in health-related vocabulary negatively correlates with receiving informational support. The readability and brevity of written texts have positive relationships with incoming social support. |
Dimensionality | Perspective | Representative Variables |
---|---|---|
Lexical | the usage of characters and words in sentences | Vocabulary richness [46] (which measures how many different words one uses in communication, and the use of richer words tends to be more persuasive) |
Syntactic | grammar and the appropriate use of words in sentences | Readability of information [17] (refers to the extent to which the content can be easily understood by an intended audience) The length of information [44] (in general, longer texts can convey richer information, and text length can positively affect the amount of information received) |
Semantic | the meanings of words behind their occurrence | Sentiment [39,40] (doctors who show more emotional support may enhance patient satisfaction and trust in the doctor teams) Content-specific keywords [17] (patients can determine to what extent they discuss topics related to diseases, symptoms, treatments, and their relations) |
Pragmatic | the meaning of words and word choice in the appropriate contexts; Distinct from semantics, pragmatics concerns the choice of words used to express the same meaning | Level of language sharing [44,47] (higher levels of language sharing in a given scenario can increase communication effectiveness [44] and willingness to share knowledge [47]. Using health language that other OHC participants also use may increase one’s social acceptance and lead to better communication outcomes [17]) |
Variable Type | Variable Name | Variable Definition |
---|---|---|
Dependent variable | Patients’ Emotional Well-Being | The direction and extent of the patient’s emotional change during the consultation with the doctor’s team |
Independent variable —— Team level | Team Emotion | The emotion of the doctors’ reply text, range from 0 (negative) to 1 (positive) |
Team Perception | The percentage of perceptual words in team level usage | |
Team Bio | The percentage of biological words in team level usage | |
Team Richness | Health vocabulary richness in team level | |
Independent variable —— Individual-Leader level | Leader Emotion | The emotion of the leader’s reply text, range from 0 (negative) to 1 (positive) |
Leader Perception | The percentage of perceived words in leader’s conversations | |
Leader Bio | The percentage of biological words in leader’s conversations | |
Leader Richness | Health vocabulary richness in leader’s conversations | |
Independent variable —— Individual-Non-Leader level | Non-Leader Emotion | The emotion of the non-leader’s reply text, range from 0 (negative) to 1 (positive) |
Non-Leader Perception | The percentage of perceived words in non-leader’s conversations | |
Non-Leader Bio | The percentage of biological words in non-leader’s conversations | |
Non-Leader Richness | Health vocabulary richness in non-leader’s conversations | |
Control variables | Team Price | The natural logarithm of the team price of an online doctor team |
Team Longevity | As measured by the number of days between team inception and the deadline for data collection (log-transformed) | |
Team Size | The number of team members | |
Reply Rate | The response rate of a team answered within 24 h from a patient’s question being asked | |
Team Help | The total number of patients served by the team on the online medical consultation platform since its inception (log-transformed) | |
Team Comprehensive Level | Obtained by taking the mean values of the title level, the hospital level, and the city level of the team members, respectively, and standardizing them, and then summing them | |
Leader Involvement Ratio | The ratio of the number of consultations involving team leaders to the total number of team consultations | |
Disease Seriousness | The difficulty of treatment of the disease, serious diseases such as cancer, leukemia, uremia, AIDS, and heart disease were set to 1, and other diseases were set to 0 | |
Patient Health Terms | The use of health-related terms by patients. For each interaction text of the patient, the number of perceptual and biological words as a percentage of the total text content was calculated using TextMind (see Appendix A) and then aggregated to the team level. |
Variable | Max | Min | Average | Standard |
---|---|---|---|---|
Patient’s Emotional Well-Being | 0.885 | −0.937 | 0.017 | 0.145 |
Team Richness | 10.577 | 1.176 | 6.486 | 1.596 |
Team Perception | 0.126 | 0.000 | 0.013 | 0.009 |
Team Bio | 0.272 | 0.000 | 0.094 | 0.030 |
Team Emotion | 0.988 | 0.027 | 0.567 | 0.103 |
Leader Richness | 10.297 | 0.778 | 5.535 | 1.707 |
Leader Perception | 0.134 | 0.000 | 0.013 | 0.011 |
Leader Bio | 0.444 | 0.000 | 0.094 | 0.039 |
Leader Emotion | 0.999 | 0.002 | 0.563 | 0.124 |
Non-Leader Richness | 9.736 | 0.954 | 5.790 | 1.737 |
Non-Leader Perception | 0.176 | 0.000 | 0.015 | 0.013 |
Non-Leader Bio | 0.400 | 0.000 | 0.095 | 0.037 |
Non-Leader Emotion | 0.964 | 0.021 | 0.543 | 0.086 |
Team Price | 6.868 | 1.792 | 3.851 | 0.965 |
Team Longevity | 6.813 | 5.153 | 6.397 | 0.268 |
Team Size | 10.000 | 2.000 | 3.519 | 1.509 |
Reply Rate | 1.000 | 0.000 | 0.873 | 0.218 |
Patient Health Terms | 0.542 | 0.000 | 0.119 | 0.037 |
Leader Involvement Ratio | 1.000 | 0.000 | 0.652 | 0.360 |
Disease Seriousness | 1.000 | 0.000 | 0.218 | 0.274 |
Team Comprehensive Level | 1.359 | −4.059 | 0.004 | 0.588 |
Team Help | 6.695 | 0.693 | 3.134 | 1.215 |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1. Team Price | 1 | ||||||||||||
2. Team Longevity | 0.206 *** | 1 | |||||||||||
3. Team Size | 0.118 *** | 0.211 *** | 1 | ||||||||||
4. Reply Rate | 0.065 ** | 0.00900 | −0.0140 | 1 | |||||||||
5. Team Help | 0.252 *** | 0.401 *** | 0.239 *** | 0.086 *** | 1 | ||||||||
6. Team Comprehensive Level | 0.068 ** | 0.0360 | −0.086 *** | −0.049 * | 0.0150 | 1 | |||||||
7. Leader Involvement Ratio | 0.144 *** | 0.051 * | −0.093 *** | 0.055 ** | −0.0220 | −0.076 *** | 1 | ||||||
8. Disease Seriousness | 0.099 *** | −0.074 *** | 0.097 *** | −0.078 *** | −0.052 * | 0.088 *** | −0.046 * | 1 | |||||
9. Patient Health Terms | −0.159 *** | −0.0350 | −0.0350 | −0.0140 | −0.104 *** | −0.071 ** | −0.073 *** | −0.067 ** | 1 | ||||
10. Team Richness | 0.138 *** | 0.261 *** | 0.156 *** | 0.051 * | 0.512 *** | −0.00600 | −0.0130 | −0.064 ** | −0.0190 | 1 | |||
11. Team Perception | −0.052 * | −0.063 ** | −0.0410 | 0.00400 | −0.00900 | −0.049 * | 0.0170 | −0.122 *** | 0.089 *** | 0.055 ** | 1 | ||
12. Team Bio | −0.092 *** | −0.00700 | 0.0260 | −0.0180 | −0.103 *** | −0.00700 | −0.0310 | 0.0140 | 0.379 *** | 0.00800 | −0.049 * | 1 | |
13. Team Emotion | 0.0100 | 0.0420 | 0.066 ** | −0.0340 | −0.085 *** | 0.085 *** | −0.062 ** | 0.097 *** | 0.0410 | 0.00300 | −0.0380 | 0.173 *** | 1 |
Hypothetical | Variables | Model 1 Basic Model | Model 2 Team-Level | Model 3 Individual-Level |
---|---|---|---|---|
Control | ||||
Team Price | 0.009 ** | 0.009 ** | 0.01 ** | |
(0.048) | (0.036) | (0.015) | ||
Team Longevity | −0.037 ** | −0.041 ** | −0.031 * | |
(0.027) | (0.014) | (0.057) | ||
Team Size | 0.001 | 0 | 0.002 | |
(0.695) | (0.958) | (0.495) | ||
Reply Rate | −0.008 | −0.007 | −0.011 | |
(0.647) | (0.694) | (0.537) | ||
Team Help | 0.003 | 0.005 | 0 | |
(0.442) | (0.225) | (0.962) | ||
Team Comprehensive Level | −0.001 | −0.004 | 0 | |
(0.857) | (0.604) | (0.998) | ||
Leader Involvement Ratio | −0.004 | −0.002 | −0.015 | |
(0.757) | (0.856) | (0.248) | ||
Disease Seriousness | −0.034 ** | −0.035 ** | −0.011 | |
(0.025) | (0.02) | (0.454) | ||
Patient Health Terms | −0.145 | −0.341 *** | −0.28 ** | |
(0.185) | (0.004) | (0.012) | ||
Main Effects | ||||
H1a | Team Richness | 0.001 | ||
(0.839) | ||||
H2a | Team Perception | 10.498 *** | ||
(0.001) | ||||
H3a | Team Bio | 0.488 *** | ||
(0.001) | ||||
H4a | Team Emotion | 0.17 *** | ||
(0) | ||||
H1b | Leader Richness | 0.003 | ||
(0.297) | ||||
H2b | Leader Perception | 0.585 | ||
(0.101) | ||||
H3b | Leader Bio | 0.069 | ||
(0.503) | ||||
H4b | Leader Emotion | 0.137 *** | ||
(0) | ||||
H1c | Non-Leader Richness | 0.001 | ||
(0.829) | ||||
H2c | Non-Leader Perception | 0.671 ** | ||
(0.031) | ||||
H3c | Non-Leader Bio | 0.358 *** | ||
(0.001) | ||||
H4c | Non-Leader Emotion | −0.421 *** | ||
(0) | ||||
Constant | 0.241 ** | 0.117 | 0.295 *** | |
(0.02) | (0.26) | (0.004) | ||
R-squared | 0.011 | 0.044 | 0.084 |
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Liu, X.; Zhou, S.; Chi, X. How Do Team-Level and Individual-Level Linguistic Styles Affect Patients’ Emotional Well-Being—Evidence from Online Doctor Teams. Int. J. Environ. Res. Public Health 2023, 20, 1915. https://doi.org/10.3390/ijerph20031915
Liu X, Zhou S, Chi X. How Do Team-Level and Individual-Level Linguistic Styles Affect Patients’ Emotional Well-Being—Evidence from Online Doctor Teams. International Journal of Environmental Research and Public Health. 2023; 20(3):1915. https://doi.org/10.3390/ijerph20031915
Chicago/Turabian StyleLiu, Xuan, Shuqing Zhou, and Xiaotong Chi. 2023. "How Do Team-Level and Individual-Level Linguistic Styles Affect Patients’ Emotional Well-Being—Evidence from Online Doctor Teams" International Journal of Environmental Research and Public Health 20, no. 3: 1915. https://doi.org/10.3390/ijerph20031915