Financial Implication of COVID-19: A Story of Malaysian Dental Practitioner
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) has caused series of lockdown in Malaysia which led to the significant financial impact to dental practitioner in Malaysia. Objectives: The aim of this study is to investigate factors affecting dental practice and its implication to financial situation during the pandemic in Malaysia. Methods: Registered Malaysian dental practitioners were invited to participate in online questionnaire via google form. Descriptive and Pearson’s chi-square test analysis were conducted (p<0.05). Results: 468 of dental practitioners had responded to our survey. More than three-quarter of dental practitioners worked in government sectors while almost a quarter worked in private sectors. 49.2% of respondents opened for emergency cases only during movement control order (MCO), 42.1 % of them limit for dental emergency, appointment-based cases and non-aerosol generated procedure during Conditional MCO and 62.5% operated as usual following a strict standard operating procedure during Recovery MCO. More than three quarter of private dental practitioners indicated that pandemic affected their daily monetary income (p<0.001) while most of them had to spend other source of income (p=0.004). All working sectors dictated that the working volume and number of patients had statistically decrease (p<0.05). Conclusion: COVID-19 pandemic had an impact on the practice of dentistry and financial position especially for private dental practitioners. Major government assistance is important to reduce the burden of dental practitioner and preserving their future practice.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| 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".