Facilitators of and Barriers to Global Digital Oral Health: Mixed Methods Study
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".