Assessment of Role of PPE in Preventing the Spread of Infection among the Dental Surgeons: A Prospective Study
Bibliographic record
Abstract
Although there is easy accessibility of infection control measures and recommendation regarding the PPE, most of the dentists failed to practice appropriate infection control measures. The aim of the current survey was conducted to assess the knowledge, perception, and attitude regarding the role of PPE among the dental care professionals in COVID-19. This is cross-sectional web-based questionnaire survey conducted among dental care professionals in Tamil Nadu. The self-administered questions related to the PPE infection control measures were collected from 500 subjects. The statistical analysis was done using Statistical Package for Social Sciences SPSS (V 22.0). The frequency distribution was computed. This survey revealed that all the 500 (100%) respondents had awareness about the role of PPE in COVID-19 pandemic. Among the 500 study subjects, 93.2% had well-known knowledge about PPE, 60.4% of dentist strictly adheres to the use of PPE in routine dental practice, 80.2% of dentist mentioned PPE is safe and effective against spread of infection, and 93.4% of dentist had awareness about donning and doffing. Conclusion: From the beginning of this COVID-19, information provided by the health organization like CDC and WHO regarding the role of PPE had positive impact among the dental care professionals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".