Indicators of integrating oral health care within universal health coverage and general health care in low-, middle-, and high-income countries: a scoping review
Bibliographic record
Abstract
BACKGROUND: The World Health Organization (WHO) has recently devoted special attention to oral health and oral health care recommending the latter becoming part of universal health coverage (UHC) so as to reduce oral health inequalities across the globe. In this context, as countries consider acting on this recommendation, it is essential to develop a monitoring framework to measure the progress of integrating oral health/health care into UHC. This study aimed to identify existing measures in the literature that could be used to indicate oral health/health care integration within UHC across a range of low-, middle- and high-income countries. METHODS: A scoping review was conducted by searching MEDLINE via Ovid, CINAHL, and Ovid Global Health databases. There were no quality or publication date restrictions in the search strategy. An initial search by an academic librarian was followed by the independent reviewing of all identified articles by two authors for inclusion or exclusion based on the relevance of the work in the articles to the review topic. The included articles were all published in English. Articles concerning which the reviewers disagreed on inclusion or exclusion were reviewed by a third author, and subsequent discussion resulted in agreement on which articles were to be included and excluded. The included articles were reviewed to identify relevant indicators and the results were descriptively mapped using a simple frequency count of the indicators. RESULTS: The 83 included articles included work from a wide range of 32 countries and were published between 1995 and 2021. The review identified 54 indicators divided into 15 categories. The most frequently reported indicators were in the following categories: dental service utilization, oral health status, cost/service/population coverage, finances, health facility access, and workforce and human resources. This study was limited by the databases searched and the use of English-language publications only. CONCLUSIONS: This scoping review identified 54 indicators in a wide range of 15 categories of indicators that have the potential to be used to evaluate the integration of oral health/health care into UHC across a wide range of countries.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".