Potential of Mobile Technology to Relieve the Urgent Mental Health Needs in China: Web-Based Survey
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».