Global mapping of oral health plans, programmes and policies in countries with universal health coverage: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: This review aims to map oral health plans, programmes and policies worldwide in countries with universal health coverage. METHODS AND ANALYSIS: This protocol describes a scoping review that will follow the Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review checklist, guided by the PCC framework: Population-countries with universal health coverage (78 globally recognised); Concept-oral health plans, programmes and policies; Context-integration into health systems. Searches will be conducted in MEDLINE (PubMed), Scopus, Web of Science, Embase, Health System Evidence and Epistemonikos, with no restrictions on date, language or study type. Grey literature will be accessed through Google Scholar, OpenThesis and the Brazilian Digital Library of Theses and Dissertations. Official documents from ministries of health and international bodies, including the WHO and the International Monetary Fund, will also be reviewed. Two independent reviewers will screen titles and abstracts; a third will resolve disagreements. Eligible records will undergo full-text review. Data will be extracted into predefined categories reflecting health system components: population, structure, services, governance and oral health indicators. Results will be presented using tables, charts and figures to illustrate strategies and innovations. ETHICS AND DISSEMINATION: This review does not involve primary data collection and does not require ethical approval. Results will be disseminated through a peer-reviewed publication and presentations at academic conferences and scientific events. STUDY REGISTRATION: Open Science Framework (DOI 10.17605/OSF.IO/RCP8N).
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 | 0.165 | 0.117 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.097 | 0.027 |
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".