Healthcare practitioners' acceptance of using telehealth in the Kingdom of Saudi Arabia: an application of the unified theory of acceptance and use of technology model
Notice bibliographique
Résumé
Introduction Telehealth offers several advantages over traditional in-person clinic visits. Despite its potential benefits, some barriers affect the optimal use of telehealth. Understanding healthcare practitioners' (HCPs) acceptance of telehealth is essential to ensure the successful, high-quality, and safe implementation of telehealth programs. However, a comprehensive, theory-driven understanding of the factors influencing HCPs' acceptance of telehealth in the Kingdom of Saudi Arabia (KSA) is lacking, hindering the development of effective implementation strategies. Therefore, this study aimed to measure telehealth acceptance among HCPs in the KSA and to identify the key predictors of their intention to use it, with a specific focus on constructs derived from the Unified Theory of Acceptance and Use of Technology (UTAUT). Methods This study was conducted from June 2024 to January 2025. HCPs working in the KSA were included. The survey was grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and consisted of four constructs: performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). In addition to behavioral intention (BI) as the dependent variable. The data analysis included performing descriptive analysis for the sociodemographic variables and multivariate logistic regression analysis. Results A total of 1,051 HCPs completed the survey. The analysis indicated that 97.8% of respondents expressed a positive intention to use telehealth in the future. Performance expectancy (PE) emerged as a significant predictor of the intention to use telehealth [adjusted odds ratio (AOR) = 4.45, p < 0.05], as did social influence (SI) (AOR = 19.25.2, p < 0.01). Furthermore, employment in military or private hospitals was associated with a significantly lower likelihood of intending to use telehealth (AOR = 0.05, p < 0.05 and AOR = 0.07, p < 0.05, respectively). Conclusion This study represents the first large-scale, theory-driven investigation of telehealth acceptance among healthcare practitioners (HCPs) across all regions of the KSA. The findings underscore the critical role of performance expectancy (PE) and social influence (SI) as significant predictors of HCPs' intention to adopt telehealth services. These insights provide valuable direction for policymakers and healthcare leaders, emphasizing the importance of fostering supportive professional environments and highlighting perceived benefits to enhance telehealth adoption. To address concerns related to effort expectancy and the facilitating conditions, telehealth developers should prioritize user-friendly designs and provide accessible and responsive IT support for users.
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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 | 0,001 |
| 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 | 0,000 |
| 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 ».