Changes in Oral Health Policies and Guidelines During the COVID-19 Pandemic
Bibliographic record
Abstract
The aim of this study was to describe the changes in oral health policies and guidelines in response to the Coronavirus disease 2019 (COVID-19) pandemic in different countries and regions around the world. Information on oral health policies and guidelines from 9 countries (Canada, China including Hong Kong, Egypt, India, Japan, New Zealand, Nigeria, Switzerland, and Thailand) were summarized, and sources of the information were mostly the national or regional health authorities and/or dental council/associations. The changes made to the oral health guidelines depended on the severity of the COVID-19 pandemic. This included suspension of non-emergency dental care services at the peak of the COVID-19 outbreak, and easing the restrictions on non-essential and elective dental care when the pandemic became under control. The COVID-19 risk mitigation strategies include strict adherence to infection control practices (use of hand sanitizers, facemask and maintaining social distancing), reducing the amount of aerosol production in the dental setting, and managing the quality of air in the dental treatment rooms by reducing the use of air conditioners and improving air exchange. The COVID-19 pandemic has shown a major impact on dental practice. Dental professionals are trying to adapt to the new norms, while the medium to long-term impact of COVID-19 on dentistry needs further investigation.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".