Assessing Elderly User Preference for Telehealth Solutions in China: Exploratory Quantitative Study
Notice bibliographique
Résumé
BACKGROUND: In the next 15 to 20 years, the Chinese population will reach a plateau and start to decline. With the changing family structure and rushed urbanization policies, there will be greater demand for high-quality medical resources at urban centers and home-based elderly care driven by telehealth solutions. This paper describes an exploratory study regarding elderly users' preference for telehealth solutions in the next 5 to 10 years in 4 cities, Shenzhen, Hangzhou, Wuhan, and Yichang. OBJECTIVE: The goal is to analyze why users choose telehealth solutions over traditional health solutions based on a questionnaire study involving 4 age groups (50-60, 61-70, 71-80, and 80+) in 4 cities (Shenzhen, Hangzhou, Wuhan, and Yichang) in the next 10 to 20 years. The legal retirement age for female workers in China is 50 to 55 years and 60 years for male workers. To simulate reality in terms of elderly care in China, the authors use the Chinese definition of elderly for employees, defined as being 50 to 60 years old rather than 65 years, as defined by the World Health Organization. METHODS: The questionnaires were collected from Shenzhen, Hangzhou, Wuhan, and Yichang randomly with 390 valid data samples. The questionnaire consists of 31 questions distributed offline on tablet devices by local investigators. Subsequently, Stata 16.0 and SPSS 24.0 were used to analyze the data. O-logit ordered regression and principal component analysis (PCA) were the main theoretical models used. The study is currently in the exploratory stage and therefore does not seek generalization of the results. RESULTS: Approximately 71.09% (280/390) of the respondents reported having at least 1 type of chronic disease. We started with PCA and categorized all Likert scale variables into 3 factors. The influence of demographic variables on Factors 1, 2, and 3 was verified using analysis of variance (ANOVA) and t tests. The ordered logit regression results suggest that health-related motivations are positively related to the willingness to use telehealth solutions, and trust on data collected from telehealth solutions is negatively correlated with the willingness to use telehealth solutions. CONCLUSIONS: The findings suggest that there is a need to address the gap in community health care and ensure health care continuity between different levels of health care institutions in China by providing telehealth solutions. Meanwhile, telehealth solution providers must focus on improving users' health awareness and lower health risk for chronic diseases by addressing lifestyle changes such as regular exercise and social activity. The interoperability between the electronic health record system and telehealth solutions remains a hurdle for telehealth solutions to add value in health care. The hurdle is that doctors neither adjust health care plans nor diagnose based on data collected by telehealth solutions.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».