Potential of Mobile Technology to Relieve the Urgent Mental Health Needs in China: Web-Based Survey
Bibliographic record
Abstract
Background With the rapid development of information technology and mobile devices, an increasing number of mobile medical services and platforms have emerged. However, China’s current mental health situation necessitates further discussion and research on how to provide more patient-centered services in the face of many challenges and opportunities. Objective This study aims to explore the attitudes and preferences of mental health service stakeholders regarding mobile mental health services and discuss the challenges and opportunities faced by mobile technology developers in China. Methods A web-based survey was conducted by following the Checklist for Reporting Results of Internet E-Surveys (CHERRIES) checklist. A total of 586 valid questionnaires were collected. Respondents included 184 patients or their family members, 225 mental health professionals, and 177 people from the general population. Data analysis was completed using SPSS 24.0. Results Among the various problems perceived regarding the current mental health medical environment, difficulty in finding appropriate psychologists and limited visit times ranked highest. Social media (n=380/586, 64.9%) was the most preferred platform among all participants, whereas professionals showed a higher preference for smartphone apps (n=169/225, 75.1%). Professional instruction, psychological consultation, and mental health education (ranked top 3) were the most commonly identified needs. Mental health professionals generally emphasized more on treatment-related mobile mental health service needs, especially medication reminders (χ22=70.7; P<.001), symptom monitoring (χ22=24.0; P<.001), and access to mental health resources (χ22=38.6; P<.001). However, patients and their family members focused more on convenient web-based prescriptions (χ22=7.7; P=.02), with the general population interested in web-based psychological consultation (χ22=23.1; P<.001) and mental health knowledge (χ22=9.1; P=.01). Almost half of the participants regarded mobile mental health services as highly acceptable or supported their use, but less than 30% of participants thought mobile mental health services might be very helpful. Concerns about mobile mental health mainly focused on information security. Service receivers also suspected the quality and professionalism of content, and mental health professionals were worried about time and energy consumption as well as medical safety. Conclusions In terms of service flow, mobile services could be used to expand service time and improve efficiency before and after diagnosis. More individualized mobile mental health service content in more acceptable forms should be developed to meet the various needs of different mental health stakeholders. Multidisciplinary training and communication could be incorporated to facilitate the integration and cooperation of more well-rounded service teams. A standard medical record system and data format would better promote the development of future intelligent medical care. Issues such as ensuring service quality, solving safety risks, and better integrating mobile services with regular medical workflows also need to be addressed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".