Abstract
Objective
Internet-based mental health services (i-MHS) have been widely provided to the public during the pandemic. However, people's engagement with i-MHS remains unclear. This study aimed to examine the Chinese population's knowledge, attitudes, and use intentions regarding i-MHS and determine their relations by serial mediation models.
Methods
The public's knowledge, attitudes, intention to use i-MHS and psychological impact of the pandemic were assessed. An online survey was administered to respondents from mainland China (N = 2543).
Results
Of the participants, 53.9 % exhibited some familiarity with i-MHS, while 62.4 % perceived these services as somewhat or very helpful, and 53.2 % were willing or very willing to use them. Serial mediation analyses indicated that the psychological impact of the pandemic indirectly related to the intention to use i-MHS. Knowledge and attitudes toward i-MHS sequentially mediated this relation [χ2(61) = 179.359, P < .001; χ2/df = 2.940; CFI = 0.996; RMSEA = 0.028].
Conclusions
This study underscores the critical role of knowledge in shaping positive attitudes and intentions to use i-MHS, emphasizing the need for robust mental healthcare promotion strategies to raise knowledge and maximize the benefits of i-MHS.
Keywords: Mental health services, Internet-delivered, Mental health, Help-seeking
Highlights
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53.9 % reported familiarity with internet-based mental health services (i-MHS), and 53.2 % were willing to use them.
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62.4 % perceived i-MHS as somewhat or very helpful, indicating general acceptance and potential for widespread adoption.
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Knowledge of i-MHS, directly and viaattitudes, influenced intentions to use them, underscoring knowledge enhancement needs.
1. Introduction
Although mental disorders have emerged as a prominent contributor to the global burden of disease, a substantial mental health treatment gap persists, with 32.2 % to 78.1 % of individuals in need of mental health services lacking access to care (Kohn et al., 2004; Saxena et al., 2007; Walker et al., 2015). In a prior study of 63,004 Chinese adults, 24 % with diagnosable mental illness experienced moderate to severe disability, while only 8 % sought professional help, and 5 % consulted a mental health professional (Phillips et al., 2009). In recent years, the rapid dissemination of technology and the popularization of the Internet has driven the rapid growth and widespread adoption of internet-based mental health services (i-MHS). I-MHS encompass various online platforms and resources aimed at providing mental health support, such as helplines, online counseling services, self-help interventions, psychological education, and the dissemination of relevant government or committee documents. For instance, an Australian study found that online services promoted help-seeking behavior and facilitated early mental health intervention among youth (Collin et al., 2011). This trend has been further accentuated by a heightened focus on various forms of internet mental health support, particularly during the COVID-19 pandemic (Liu et al., 2020).
Internet-based mental health services offer distinct advantages such as improved accessibility, confidentiality, cost-effectiveness, and comparable efficacy to traditional services (Price et al., 2014; Krausz et al., 2019). Notably, the availability of i-MHS has expanded considerably during the pandemic, reflecting their transformative potential in addressing the evolving challenges of providing mental health support. Nevertheless, the extent to which the general population appropriately engages with i-MHS lacks empirical exploration. Some individuals may be unaware of the availability of i-MHS or may still hold stigmatizing beliefs about seeking help for mental health issues. This underutilization could potentially have far-reaching implications, impacting resource efficiency and potentially exacerbating the burden of mental disorders (Hendriks et al., 2015; Wells et al., 1989; Herrman, 2001).
Moreover, the mechanisms underlying the decision-making process regarding i-MHS engagement remain unclear (Chuenphitthayavut et al., 2020). The Uses and Gratifications Theory (UGT) suggests that individuals purposefully choose information to fulfill their unique needs (Ruggiero, 2000). It has been previously applied to study users' willingness to consult chatbots for health education (Hsu et al., 2019) and students' perceived e-learning experiences (Mondi et al., 2007). In our study, we assume that within the UGT framework, individuals facing psychological challenges tend to seek specific mental health information tailored to their circumstances. Complementing UGT, the Knowledge-Attitude-Behavior (KAB) model, an influential health education theory, contends that knowledge and attitudes are pivotal in shaping behavioral change (Kemm, 1995). Hence, the intricate interplay between individuals' psychological condition, knowledge, attitudes, and utilization of i-MHS is underscored. However, a comprehensive model that takes all of these factors into account has not been examined.
In the current study, we aimed to assess the public's knowledge, attitudes, and intentions toward i-MHS use, focusing on engaging with five types of i-MHS. We conducted serial mediation analyses using a structural equation model (SEM) to examine the underlying mechanisms. Based on the UGT and the KAB model, the specific research objectives were to (a) examine the positive association between psychological impact and i-MHS use intention and (b) examine the sequential mediation of knowledge and attitudes on the relationship between psychological impact and intention to use i-MHS (see Fig. 1).
Fig. 1.
Hypothesized serial mediation model.
2. Material and methods
2.1. Participants
The present study was conducted from February 7, 2020, to March 12, 2020, via the Chinese online questionnaire platform, Survey Star (“Wen Juan Xing”, www.wjx.cn). A snowball strategy was used in distribution of the questionnaire, and a recruitment poster was published and shared through leading Chinese social platforms. A total of 2543 adult residents in mainland China who completed the survey over 280 s and passed three attention-test items were included in the data analysis. Informed consent was given online and participants who completed the survey were paid 6 RMB (approximately US$0.85) The study was approved by the Ethics Committee of the School of Psychological and Cognitive Sciences, Peking University (#2020-11-06).
2.2. Measures
Participants' demographic information, knowledge, attitudes, and intention to use 18 types of i-MHS were assessed. The 18 i-MHS services were grouped into five categories: mental health helpline, online individual counseling service, online mental health intervention, online psychological education, and official released papers and documents (see Table A.1 for details).
2.2.1. The psychological impact of COVID-19
A self-rated single-item question measured perceived psychological impact (“How do you think COVID-19 has affected your mental health?”). A Likert scale with 11 response options was used from 0 = no impact at all to 10 = seriously affected, with higher scores indicating higher levels of psychological impact. The single-item measure of self-rated health has been widely used in public health research (Main et al., 2011).
2.2.2. Knowledge of i-MHS
A measure to assess knowledge of i-MHS was developed. Participants were asked: “How much do you know about [a specific type of assistance]?” for each method. A Likert scale with six response options was used: 1 = Never heard of it, 2 = I probably heard about it, 3 = I heard about it, but am not familiar with it, 4 = I have some understanding but have never used it, 5 = I have used it, but do not think I know it well, and 6 = I have used it, and I think I have a good understanding of it, with higher scores indicating better knowledge of specific types of i-MHS. Scores ranged from 18 to 108. Cronbach's α for the 18 questions in the current sample was 0.958.
2.2.3. Attitudes toward i-MHS
A measure to assess the attitudes toward i-MHS was developed. Participants were required to answer: “In the case of this outbreak, to what extent do you think the [a specific type of assistance] would help people with mental problems?”. A Likert scale with five response options was used: 1 = useless, 2 = probably not helpful, 3 = not sure, 4 = somewhat helpful, 5 = very helpful, with higher scores indicating more positive attitudes toward i-MHS. The score ranged from 18 to 90. Cronbach's α for this measure in the current sample was 0.946.
2.2.4. Intention to use i-MHS
Since data collection was conducted at the early stage of the pandemic, we developed a measure to assess the intention to use i-MHS rather than the actual usage behavior. Participants were asked: “During this outbreak if you encountered any psychological problem, how likely would you use the following forms of mental health services …?” for the 18 services. The Likert response scale ranged from 1 = would not use it to 5 = very willing to use it, with higher scores indicating a stronger intention to use those services. The scores ranged from 18 to 90. Cronbach's α for the 18 questions with the current sample was 0.939.
2.3. Statistical analysis
Descriptive statistics were conducted using SPSS 25.0. SEM was employed to test the serial mediation model by AMOS 23.0 to reduce measurement bias. A bootstrapping method (with 5000 bootstrap samples) was used to estimate the bias-corrected 95 % confidence interval (CI) and confirm the indirect (mediating) effect. The models were constructed using the following indices of fit: χ2 value, root mean square error of approximation (RMSEA) < 0.05, and comparative fit index (CFI) > 0.95 (Hu and Bentler, 1999). Because the χ2 statistic is sensitive to sample size, the ratio of chi-square to degrees of freedom (χ2/df) with values of 3.0 or less was considered satisfactory (Hu and Bentler, 1999). Unstandardized and standardized regression coefficients were reported.
3. Results
Participants' demographic characteristics and descriptive results on study variables are presented in Table 1. The mean age of the participants was 27.48 years (SD = 8.56), with 47.1 % females and 65.6 % with a junior college education level or above. The mean and SD for the 18 i-MHS options were presented in Table A.2.
Table 1.
Characteristics of the participants (N = 2543).
| Variables | Mean/N | SD/% |
|---|---|---|
| Gender, No. (%) | ||
| Male | 1346 | 52.9 |
| Female | 1197 | 47.1 |
| Age, Mean (SD) | 27.48 | 8.56 |
| Relationship, No. (%) | ||
| In a relationship | 1108 | 43.6 |
| Not in a relationship | 1435 | 56.4 |
| Employment Status, No. (%) | ||
| Student | 830 | 32.6 |
| Unemployed | 233 | 9.2 |
| Part-time Job | 235 | 9.2 |
| Full-time Job | 1214 | 47.7 |
| Retired | 31 | 1.2 |
| Educational Level, No. (%) | ||
| Primary school and secondary school | 237 | 9.3 |
| High school and technical secondary school | 637 | 25.0 |
| Junior college | 491 | 19.3 |
| Undergraduate | 989 | 38.9 |
| Graduate | 189 | 7.4 |
| Psychological impacts of COVID-19 | 6.20 | 2.24 |
| Knowledge of i-MHS | ||
| Mental health helpline | 3.22 | 1.22 |
| Online individual counseling service | 3.32 | 1.35 |
| Online guided mental health intervention | 3.43 | 1.37 |
| Online psychological education | 3.53 | 1.36 |
| Official documents released | 3.71 | 1.46 |
| Attitudes toward i-MHS | ||
| Mental health helpline | 3.82 | 0.76 |
| Online individual counseling service | 3.64 | 0.80 |
| Online guided mental health intervention | 3.67 | 0.78 |
| Online psychological education | 3.72 | 0.79 |
| Official documents released | 3.73 | 0.92 |
| Intention to use i-MHS | ||
| Mental health helpline | 3.30 | 0.92 |
| Online individual counseling service | 3.18 | 0.94 |
| Online guided mental health intervention | 3.39 | 0.89 |
| Online psychological education | 3.40 | 0.94 |
| Official documents released | 3.55 | 1.01 |
3.1. Knowledge, attitudes, and intention to use i-MHS
Participants had a low level of knowledge about i-MHS. Only 9.7 % have ever used any type of i-MHS. Overall, 53.9 % of respondents indicated some knowledge of i-MHS. Regarding attitudes toward i-MHS, 21.3 % of participants believed that i-MHS would be very helpful for people with mental health problems, and cumulatively, 62.4 % perceived i-MHS as somewhat or very helpful. More than half (53.2 %) indicated they were “willing to use it” or “very willing to use it,” a type of i-MHS, and cumulatively 46.8 % indicated low levels of intention to use it. The percentage of responses regarding knowledge, attitudes, and intention to use i-MHS can be seen in Table A.3. Partial correlation controlling for age and gender showed that knowledge, attitudes, and intention to use i-MHS were positively correlated (r = 0.504–0.668, all Ps < 0.001).
3.2. Measurement models
Analysis of the measurement model for knowledge of i-MHS resulted in good indices of fit, χ2 (1) = 2.749, P = .097; χ2/ df = 2.749; RMSEA = 0.026, CFI = 1.00. This was significantly represented by all five indicators (all at P < .001), with standardized coefficients of 0.872, 0.853, 0.835, 0.848, and 0.697 for mental health helpline, online individual counseling service, online guided mental health intervention, psychological education, and official released papers or documents, respectively.
The measurement model for the attitudes toward i-MHS also yielded good indices of fit, χ2 (2) = 0.424, P = .809; χ2/ df = 0.212; RMSEA ≤0.0001, CFI = 1.00. It was significantly represented by all five indicators (all at P < .001), with standardized coefficients of 0.782, 0.860, 0.846, and 0.719 for attitudes toward mental health helpline, online individual counseling service, online guided mental health intervention, psychological education, and the official released papers or documents, respectively.
The measurement model for intention to use i-MHS also showed good indices of fit, χ2 (3) = 6.753, P = .080; χ2/df = 2.251; RMSEA = 0.022, CFI = 1.00. It was significantly represented by all five indicators (all at P < .001), with standardized coefficients of 0.742, 0.775, 0.795, 0.883, and 0.757 for intention to use mental health helpline, online individual counseling service, online guided mental health intervention, psychological education, and the official released papers or documents, respectively.
3.3. The serial mediation models
The serial mediation model between the psychological impact of COVID-19 and the intention to use i-MHS through knowledge and attitudes showed a good index of fit, χ2 (61) = 179.359, P < .001; χ2/df = 2.940; CFI = 0.996; RMSEA = 0.028 (see Fig. 2). Standardized and unstandardized regression coefficients for this model are summarized in Table 2, and standardized direct and indirect effects are listed in Table 3 with 95 % CIs using the bootstrap method. After adding knowledge and attitude toward i-MHS, the psychological impact of COVID-19 was no longer significantly associated with the intention to use i-MHS (b = 0.004, P = .773), indicating that knowledge and attitudes toward i-MHS fully mediated the relationship between the psychological impact of COVID-19 and intention to use i-MHS.
Fig. 2.
The serial mediation model (N = 2543), with standardized beta.
Notes. Fit statistics: χ2 (61) = 179.359, P < .001; χ2/df = 2.940; CFI = 0.996; RMSEA = 0.028. a The path coefficients were constrained to 1. Knowledge, knowledge of i-MHS; Attitude, Attitudes toward i-MHS; Use intention, Intention to use i-MHS. *** P < .001.
Table 2.
Unstandardized and standardized regression coefficients and standard errors for all pathways of the serial mediation model (N = 2543).
| Variable 1 → | Variable 2 | B | SE | β | P |
|---|---|---|---|---|---|
| The path between Latent Variables | |||||
| Psychological impact | Knowledge | 0.398 | 0.034 | 0.244 | < 0.001 |
| Knowledge | Attitude | 0.389 | 0.014 | 0.650 | < 0.001 |
| Psychological impact | Attitude | 0.017 | 0.016 | 0.017 | 0.298 |
| Psychological impact | Use Intention | 0.004 | 0.016 | 0.004 | 0.786 |
| Attitude | Use Intention | 0.615 | 0.028 | 0.598 | < 0.001 |
| Knowledge | Use Intention | 0.097 | 0.014 | 0.157 | < 0.001 |
| Measurement Model | |||||
| Use Intention | U_Helpline | 1a | 0.750 | ||
| U_Counseling | 1.452 | 0.031 | 0.872 | < 0.001 | |
| U_Guided | 1.278 | 0.029 | 0.803 | < 0.001 | |
| U_Education | 0.989 | 0.024 | 0.792 | < 0.001 | |
| U_Documents | 1.228 | 0.031 | 0.751 | < 0.001 | |
| Knowledge | K_Helpline | 1a | 0.752 | ||
| K_Counseling | 0.876 | 0.018 | 0.795 | < 0.001 | |
| K_Guided | 1.172 | 0.027 | 0.781 | < 0.001 | |
| K_Education | 1.345 | 0.03 | 0.907 | < 0.001 | |
| K_Documents | 0.941 | 0.023 | 0.780 | < 0.001 | |
| Attitude | A_Helpline | 1a | 0.723 | ||
| A_Counseling | 0.890 | 0.021 | 0.810 | < 0.001 | |
| A_Guided | 1.200 | 0.029 | 0.836 | < 0.001 | |
| A_Education | 1.267 | 0.029 | 0.873 | < 0.001 | |
| A_Documents | 0.977 | 0.025 | 0.783 | < 0.001 | |
Note.
The fixed value. K_ = the Knowledge of mental health helpline, online individual counseling service, online guided mental health intervention, psychological education, and the released government or committee documents (paper and online documents), respectively; A_ = the attitudes toward the five types of mental health services; U_ = the use intention of the five types of mental health services.
Table 3.
Standardized direct, indirect, and total effects of the psychological impact of COVID-19 on the intention to use i-MHS.
| Variable |
Direct effect |
Indirect effect |
Total effect |
||||||
|---|---|---|---|---|---|---|---|---|---|
| β | 95 % CI | P | β | 95 % CI | P | β | 95 % CI | P | |
| Effects on intention to use | |||||||||
| Psychological impact | 0.004 | 0.028–0.038 | 0.773 | 0.143 | 0.107–0.176 | 0.001 | 0.148 | 0.103–0.192 | <0.001 |
| Knowledge | 0.157 | 0.104–0.208 | <0.001 | 0.389 | 0.350–0.427 | <0.001 | 0.546 | 0.505–0.584 | <0.001 |
| Attitude | 0.598 | 0.546–0.645 | <0.001 | 0 | – | – | 0.598 | 0.546–0.645 | <0.001 |
| Effects on attitude | |||||||||
| Psychological impact | 0.017 | −0.020-0.051 | 0.386 | 0.158 | 0.129–0.188 | <0.001 | 0.176 | 0.126–0.218 | 0.001 |
| Knowledge | 0.650 | 0.618–0.681 | <0.001 | 0 | – | – | 0.650 | 0.618–0.681 | <0.001 |
| Effects on Knowledge | |||||||||
| Psychological impact | 0.244 | 0.200–0.287 | <0.001 | 0 | – | – | 0.244 | 0.200–0.287 | <0.001 |
4. Discussion
To our knowledge, this is the first study determining the relationship between knowledge, attitudes, and intentions to use i-MHS among the general population in China. Our results reveal that overall knowledge of i-MHS is limited, with approximately half of the respondents (46.2 %) reporting minimal or no knowledge of the services. Nevertheless, 62.4 % of respondents expressed a favorable attitude toward the services, and 53.2 % indicated a willingness to use them in case of future mental health difficulties. An encouraging finding from the serial mediation model is that knowledge and attitudes toward i-MHS fully mediate the relationship between the psychological impact and intention to use i-MHS. This aligns with our hypotheses and highlights the potential for interventions to enhance these mediators to improve i-MHS utilization.
In terms of engagement with i-MHS, the knowledge level of the general population in China (53.9 %) is higher than that of a national self-help website in Australia (approximately 39 %) (Jorm, 2009). Knowledge about online guided mental health interventions and online individual counseling services was notable, with 53.79 % and 50.63 % of respondents indicating knowledge, respectively. However, a recent study in Germany reported lower awareness of Internet-based psychotherapy, at only 33.1 % (Apolinário-Hagen et al., 2018a). Regarding public attitudes, our study found moderately positive perceptions, with 62.4 % of participants considering i-MHS useful or helpful, similar to findings in Germany where 65.9 % viewed internet interventions favorably (Apolinário-Hagen et al., 2018a). In Australia, an online survey revealed that 59.2 % of respondents perceived therapist-assisted i-MHS as helpful (Klein and Cook, 2010), a sentiment echoed by 58.23 % of participants in our study who found online psychotherapy helpful.
Furthermore, more than half of the respondents (53.2 %) expressed the intention to use i-MHS if they encounter mental health difficulties in the future. This percentage is higher than the 39.6 % found in an Australian community sample and college students (March et al., 2018) but lower than the 79.2 % reported in a Spanish study of university students (Marques et al., 2021). Overall, given that i-MHS is in its early stage of implementation in China, these levels of knowledge, attitudes, and intention to use i-MHS are considered acceptable and promising. Despite being a relatively new and evolving concept, i-MHS has the potential to become a crucial component of mental health care in China.
Our study also found that individuals with higher psychological distress levels were more likely to use i-MHS. This finding aligns with prior research, suggesting that COVID-related stress predicts the desire for mental health services (Richie et al., 2022). This trend can also be interpreted through the Uses and Gratifications Theory (UGT), which posits that individuals may turn to i-MHS as a means to address their mental health needs. Furthermore, the Health Belief Model (HBM) suggests that people's beliefs about the severity of a health threat and perceived susceptibility to the threat can influence their health-related behaviors (Carpenter, 2010). In this context, those with heightened distress may view the pandemic as a significant threat to their psychological well-being, motivating them to seek i-MHS to mitigate this threat.
The serial mediation analysis shows that greater knowledge of i-MHS is associated with more positive attitudes toward these services, which in turn predicts greater intention to use them. These findings support the Knowledge, Attitudes, and Behavior continuum (KAB), which suggests that knowledge acquisition is the initial step in behavior change. To promote the adoption of i-MHS, individuals need to actively obtain factual information about these services, enabling them to form beliefs and attitudes supporting their utilization. Similarly, a randomized controlled trial found that providing psychoeducational information about i-MHS improved attitudes toward these services within the general population (Apolinário-Hagen et al., 2018b). Additionally, the KAB continuum has been identified in the context of preventive behavior (Papagiannis et al., 2020) and precautionary behavior (Lee et al., 2021) to control the spread of COVID-19. In these cases, increased familiarity (e.g., perceived risk of COVID-19) is associated with more positive attitudes and preventive behaviors such as maintaining social distance.
Moreover, our study found that knowledge, in addition to its indirect influence through attitudes, also directly influences individuals' intention to use i-MHS. This direct impact may be particularly relevant during the early stages of i-MHS implementation in China, where individuals often lack pre-existing attitudes, either positive or negative, toward these services. In such cases, their intention to use these services would primarily rely on the information they acquire. Additionally, the context of the COVID-19 pandemic has underscored the importance of rational decision-making. During a crisis such as the COVID-19 pandemic, the imperative to address mental health concerns highlights the critical need for easily accessible mental health services (Xiong et al., 2020). In such times, individuals' intention to use i-MHS may be primarily driven by factual knowledge about these services, especially when individuals have limited prior exposure to i-MHS or are still developing their attitudes toward them. Recent research has emphasized the pivotal role of reliable information in shaping individuals' readiness to seek mental health support during the COVID-19 pandemic (Richie et al., 2022). Therefore, it is essential to consider these insights when developing strategies to promote i-MHS and address mental health needs across various contexts and situations.
Several limitations of the current study should be acknowledged. First, our assessment of the psychological impact of COVID-19 was based on a single-item measure of self-rated health, which could introduce bias. However, recent research has shown that the use of single-item measures of self-rated health is common in public health research (Beran and Violato, 2010) and is associated with multi-item measures of mental health (e.g., center for epidemiologic studies depression scale, geriatric depression scale-short form, and patient health questionnaire) (Jang et al., 2012; Ahmad et al., 2014). Second, the questionnaire used in this study was newly developed and has not been validated in other settings. To address potential biases, we employed SEM to mitigate measurement bias. Third, the cross-sectional design of our study precludes the examination of potential causal relationships among variables.
5. Conclusions
In conclusion, this study illuminates the relationship between the psychological impact of the COVID-19 pandemic and individuals' inclination to utilize internet-based Mental Health Services (i-MHS), highlighting people's engagement with mental health support during crisis situations. Our findings underscore the pivotal role of knowledge in shaping favorable attitudes and directly influencing individuals' intentions to use i-MHS, emphasizing the need for robust mental healthcare promotion strategies to raise knowledge and maximize the benefits of i-MHS. This research contributes to our understanding of mental healthcare in the digital age and highlights the importance of bridging the gap between knowledge, attitudes, and intentions in utilizing i-MHS to enhance mental health services.
Funding
This work was supported by Peking University ChenZhonggeng Clinical and Counseling Psychology Development Fund [2021YJ003], the National Natural Science Youth Fund Project [32000776]; and the Beijing New Sunshine Foundation in China. The authors acknowledge the contributions of participants involved in this project.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A. Appendices
Table A.1.
The 18 types of i-MHS.
| Category | Types of Internet-based mental health services |
|---|---|
| Mental health helpline (provided by) | The government |
| Universities | |
| Hospitals | |
| Private agencies | |
| Online individual counseling service | Free video or audio counseling service |
| Charged video or audio counseling service | |
| Free text counseling service | |
| Online guided mental health intervention | Mindfulness intervention |
| Writing intervention | |
| Arts or sports intervention | |
| Free mental health manuals or books | |
| Online psychological education | Radio or broadcast programs |
| Webcast | |
| Online courses | |
| TV program | |
| Official documents released (papers and online documents) | By the government |
| By an individual company | |
| By a community |
Table A.2.
Mean and SD for 18 i-MHS options.
|
Knowledge |
Attitude |
Use intention |
||||
|---|---|---|---|---|---|---|
| Category | M | SD | M | SD | M | SD |
| Mental health helpline (provided by) | ||||||
| The government | 3.25 | 1.43 | 3.87 | 0.95 | 3.30 | 1.20 |
| Universities | 3.37 | 1.39 | 3.77 | 0.91 | 3.27 | 1.18 |
| Hospitals | 3.31 | 1.44 | 4.05 | 0.89 | 3.64 | 1.12 |
| Private agencies | 2.97 | 1.47 | 3.59 | 0.97 | 3.00 | 1.18 |
| Online individual counseling service | ||||||
| Free video or audio counseling service | 3.35 | 1.52 | 3.77 | 0.92 | 3.32 | 1.14 |
| Charged video or audio counseling service | 3.17 | 1.42 | 3.44 | 0.97 | 2.70 | 1.19 |
| Free text counseling service | 3.43 | 1.50 | 3.71 | 0.95 | 3.50 | 1.12 |
| Online guided mental health intervention | ||||||
| Mindfulness intervention | 3.21 | 1.67 | 3.66 | 0.96 | 3.23 | 1.15 |
| Writing intervention | 3.31 | 1.55 | 3.56 | 0.94 | 3.24 | 1.13 |
| Arts or sports intervention | 3.51 | 1.58 | 3.70 | 0.94 | 3.48 | 1.12 |
| Free mental health manuals or books | 3.69 | 1.44 | 3.75 | 0.94 | 3.60 | 1.10 |
| Online psychological education | ||||||
| Radio or broadcast programs | 3.53 | 1.54 | 3.72 | 0.92 | 3.31 | 1.13 |
| Webcast | 3.35 | 1.52 | 3.61 | 0.93 | 3.29 | 1.12 |
| Online courses | 3.58 | 1.46 | 3.75 | 0.90 | 3.48 | 1.05 |
| TV program | 3.67 | 1.43 | 3.83 | 0.90 | 3.53 | 1.09 |
| Official documents released (papers and online documents) | ||||||
| By the government | 3.71 | 1.57 | 3.75 | 1.03 | 3.49 | 1.16 |
| By an individual company | 3.62 | 1.58 | 3.65 | 0.99 | 3.53 | 1.10 |
| By a community | 3.81 | 1.59 | 3.79 | 1.01 | 3.63 | 1.09 |
Table A.3.
Percentage of responses regarding knowledge, attitudes, and intention to use i-MHS (N = 2543).
| Variables | Responses | Percentage of the total sample (%) |
|---|---|---|
| Knowledge | Never heard of it | 16.8 |
| I probably have heard about it | 10.2 | |
| I have ever heard about it, but I am not familiar with it | 19.2 | |
| I have some understanding, but never used it | 30.2 | |
| I have used it, but I do not think I know it well | 14.0 | |
| I have used it, and I think I have a good understanding of it | 9.7 | |
| Attitudes | Useless | 2.5 |
| Probably not helpful | 6.6 | |
| Not sure | 28.5 | |
| Somewhat helpful | 41.1 | |
| Very helpful | 21.3 | |
| Use intention | Would not use it | 7.0 |
| May not use it | 18.4 | |
| Not sure | 21.4 | |
| Willing to use it | 37.5 | |
| Very willing to use it | 15.7 |
Note. The figures were calculated by averaging the percentages of the sum of 18 different types of services.
Table A.4.
Percentage of responses regarding knowledge, attitudes, and intention to use i-MHS of five domain (N = 2543).
| Responses |
Percentage of the total sample (%) |
|||||
|---|---|---|---|---|---|---|
| Helpline | Counseling | Intervention | Education | Documents | ||
| Knowledge | Never heard of it | 18.18 | 17.57 | 18.63 | 14.65 | 14.53 |
| I probably have heard about it | 11.80 | 11.10 | 9.65 | 9.65 | 8.33 | |
| I have ever heard about it, but I am not familiar with it | 21.80 | 20.67 | 17.95 | 18.73 | 16.47 | |
| I have some understanding, but never used it | 32.15 | 31.37 | 27.83 | 31.73 | 27.33 | |
| I have used it, but I do not think I know it well | 9.80 | 11.23 | 15.85 | 15.08 | 18.70 | |
| I have used it, and I think I have a good understanding of it | 6.30 | 8.03 | 10.10 | 10.10 | 14.67 | |
| Attitudes | Useless | 1.83 | 2.77 | 2.73 | 2.15 | 3.27 |
| Probably not helpful | 6.00 | 7.33 | 6.58 | 6.05 | 7.60 | |
| Not sure | 26.10 | 31.67 | 30.95 | 28.35 | 25.30 | |
| Somewhat helpful | 40.33 | 39.70 | 40.73 | 44.15 | 40.07 | |
| Very helpful | 25.78 | 18.53 | 19.00 | 19.30 | 23.77 | |
| Behavior | Would not use it | 7.88 | 2.63 | 6.18 | 5.80 | 6.03 |
| May not use it | 20.43 | 6.81 | 17.98 | 17.15 | 13.53 | |
| Not sure | 21.50 | 7.17 | 22.85 | 21.98 | 19.00 | |
| Willing to use it | 34.05 | 11.35 | 36.88 | 41.13 | 42.43 | |
| Very willing to use it | 16.20 | 5.40 | 16.13 | 13.93 | 19.00 | |
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