Facilitators of and Barriers to Global Digital Oral Health: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Digital oral health builds on the broader framework of eHealth, leveraging digital technologies to improve patient care, increase access to dental services, and enhance oral health outcomes. However, health care organizations and institutions encounter challenges in implementing digital oral health interventions across various levels. Addressing these challenges requires a comprehensive understanding of the barriers and facilitators that influence its successful adoption. OBJECTIVE: This study aimed to explore the facilitators of and barriers to the implementation of digital oral health programs from the perspective of chief dental officers from countries across the World Health Organization (WHO) regions. METHODS: This study is part of a broader investigation into global readiness for digital oral health. Participants were the 144 chief dental officers or designated oral health officials within ministries of health across the 6 WHO regions. An explanatory sequential mixed methods design was used across 2 phases. In the quantitative phase, an online survey was administered using the WHO's global survey on eHealth instrument. Some items were modified slightly to be applied to the field of dentistry. Descriptive statistics were used to present the quantitative data. In the qualitative phase, data were collected through virtual interviews, using an interview guide developed based on preliminary findings from the quantitative phase, the technology acceptance model, and the eHealth readiness assessment tool. The qualitative data were analyzed using thematic analysis. RESULTS: The survey response rate was 70.1% (101/144). The qualitative phase involved in-depth interviews with 15 participants. The findings were integrated under 2 broad themes of facilitators and barriers. Perceived facilitators included the existence of national policies and guidelines on eHealth. Approximately 63.9% (53/83) of the respondents indicated the presence of a national oral health policy in their countries. Capacity building, motivation of health care providers and academic leadership, digital health training for students or professionals, and WHO support to implement the mOral Health program were the other facilitators. The strongest barriers were a lack of funding to develop and support digital health programs, lack of norms and standards to guarantee application interoperability, and lack of equipment and/or connectivity. Approximately 45.1% (37/82) of the participants reported having government-sponsored mobile health programs, while 31.7% (26/82) reported having no financial support for the implementation of national digital oral health programs. Furthermore, lack of evidence on the effectiveness and cost-effectiveness of programs was highlighted as a barrier by 73.8% (59/80) and 73% (57/78) of the participants, respectively. CONCLUSIONS: The results of this study enabled the identification of key barriers to and enablers of the implementation of digital oral health programs in WHO member countries. Supportive governmental policies and adequate funding and investment in digital infrastructure and technologies are essential to mitigate digital oral health-related challenges.
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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 | 0,022 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».