COVID‐19’s impact on private practice and academic dentistry in North America
Bibliographic record
Abstract
The COVID-19 pandemic is a major public health crisis for countries around the world. In response to this global outbreak, the World Health Organization declared a public health emergency of international concern. Dental professionals are especially at high risk of contracting the COVID-19 virus due to the unique nature of dentistry, more specifically, exposure to aerosols and droplets. When it comes to dental emergencies, it was crucial to maintain urgent dental care services operational to help reduce the burden on our healthcare system and hospitals already under pressure. The COVID-19 pandemic has significantly impacted how dentistry is practiced in North America in both the private practice and academic settings. This article shares the perspectives of dentists practicing in private practice and clinician-researchers in academic dental institutions. More specifically, we discuss about measures implemented to minimize risks of disease transmission, challenges in emergency dental care, impact on patients, as well as impact on the professional and personal lives of the dental team during the COVID-19 crisis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".