The Impact of COVID-19 on Parental Perception of Oral Health-Related Quality of Life of Children: A Comparison of a Sample from Saudi Arabia and Kuwait
Bibliographic record
Abstract
Background and Aim. The COVID-19 pandemic has impacted access to dental care for children over the world. This study aimed to assess the impact of the COVID-19 pandemic on parentally reported oral health-related quality of life (OHRQoL) in Riyadh, Saudi Arabia and Kuwait City, Kuwait. Materials and Methods. OHRQoL was measured using a a validated Arabic version of the Child Oral Health Impact Profile (COHIP). Parents of children aged between 5 and 9 years were administered questionnaire during the COVID-19 pandemic. The responses were compared across the different domains of the questionnaire between the two countries using the Mann–Whitney U test. Differences were also tested between the parents of males and females separately in each city. The correlation of the COHIP scores with the age of the child was done using the Spearman’s rho. Results. No significant differences in overall COHIP scores were found between the parents in Riyadh and Kuwait City ( p > 0.05 ). There were significant gender differences observed across domains in Kuwait ( p = 0.030 ) but not in Riyadh ( p = 0.295 ). There was also a significant negative correlation between the different COHIP domains in Kuwait but not Riyadh. Conclusion. There is a greater gender difference and age correlation of OHRQoL among the population studied in Kuwait City when compared to those in Riyadh.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".