Dentists' Experiences and Dental Care in the COVID-19 Pandemic: Insights from Nova Scotia, Canada.
Bibliographic record
Abstract
BACKGROUND: This study aimed to describe dental care provision and the perceptions of dentists in Nova Scotia, Canada, during 1 week of the COVID-19 pandemic, shortly after the closing down of non-emergency, in-person care. METHODS: A survey was distributed to all 542 registered dentists in Nova Scotia, asking about dental care provision during 19-25 April 2020. Most answers were categorical, and descriptive analyses of these were performed. Data from the 1 open-ended question were analyzed using an inductive approach to identify themes. RESULTS: The response rate was 43% (n = 235). Most dentists (181) provided care but only 13 provided in-person care. From the open-ended question, 4 concerns emerged: communication from the regulatory authority; respondents' health and that of their staff; the health of and access to care for patients; and the future of their business. CONCLUSION: Most respondents remained engaged in non-in-person dental care using various modes. They expressed concerns about their health and that of their staff and patients as well as about the future of their practice. PRACTICAL IMPLICATIONS: Dentists and dental regulatory authorities should engage in discussions to promote the health of dental staff and patients and quality of care during the chronic phase of the pandemic.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".