Dental service utilization and the COVID-19 pandemic, a micro-data analysis
Bibliographic record
Abstract
BACKGROUND: Global crises and disease pandemics, such as COVID-19, negatively affect dental care utilization by several factors, such as infection anxiety, disrupted supply chains, economic contraction, and household income reduction. Exploring the pattern of this effect can help policy makers to be prepared for future crises. The present study aimed to investigate the financial impact of COVID-19 disruptions on dental service utilization. METHODS: Data on the number of dental services offered in Dental School Clinics of Tehran University of Medical Sciences was collected over a period of two years, before and after the initial COVID-19 outbreak in Iran. School of Dentistry operates two clinics; one with competitive service fees and one with subsidies. Regression analyses were performed to determine the effect of the pandemic on the number of dental services divided by dental treatment groups and these clinics. The analyses were adjusted for seasonal patterns and the capacity of the clinics. RESULTS: There was a significant drop in dental services offered in both clinics across all dental groups in the post-COVID period (on average, 77 (39.44%) fewer services per day). The majority of the procedure loss happened in the Private clinic. Adjusting for seasonal patterns and the service capacity, regression results documented 54% and 12% service loss in Private and Subsidized clinics following the pandemic, respectively. Difference-in-difference analysis documented that the Subsidized clinic performed 40% more treatments than the Private clinic in the post-COVID period. CONCLUSIONS: Pandemic -reduction in dental care utilization could have long-term ramifications for the oral health of the population, and policymakers need to provide supportive packages to the affected segments of the economy to reverse this trend.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".