Orthodontic Patients’ Perception of Orthodontic Office Changes during COVID-19 Pandemic in Brazil: A National Cross-Sectional Survey
Bibliographic record
Abstract
There is still limited information regarding patients’ perception of the dental approach changes in the pandemic circumstance. Therefore, the aims of this study were, firstly, to evaluate patient perception regarding the COVID-19 infection risk in the orthodontic office in Brazil, and to assess the influence of age in infection risk perception. Orthodontic patients from five states answered an online questionnaire, anonymously, about quarantine behavior, perception of the infection risk in the orthodontic office, as well as the apparent need for the new biosafety approach. Descriptive analyses were performed for each question. Correlations between age and concern of getting infected were calculated with Spearman correlation tests. There were 406 responses. Most patients respected the quarantine, and 93.10% of those who were scheduled for appointments realized that their appointment would be safe enough. From the total, 83.99%, 84.98%, 89.90%, and 95.81% of patients judged, respectively, health status checks by phone, temperature checking, disposable coat, and face shield, as necessary. Only 6.40% reported an increase in the concern of returning to appointments. The younger the patient, the greater the concern of getting infected in future appointments (p = 0.042). Most patients were confident in the professional care before the appointment. The new biosafety approach was well accepted by the majority, with less agreement with temperature checking and the use of disposable coats. The younger the patient, the greater the concern of getting infected in future appointments. The rate of patients with risk factors for COVID-19 was 14.77%.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".