Virtual dental consultation services during the covid-19 pandemic: a review of the Non-Insured Health Benefits (NIHB) dental claims database
Bibliographic record
Abstract
Introduction: The Covid-19 pandemic amplified access to care challenges faced by many priority populations, including First Nations and Inuit Peoples. During wave 1 of the pandemic in Canada, dental clinicians were mandated to postpone non-emergency dental services, and in many parts of the country, all dental operations ceased. Concerning this, the Non-Insured Health Benefits (NIHB) program approved “virtual dental consultation services” and decided to temporarily cover fees for consultation services by phone or other virtual methods to help assess clients’ needs and facilitate access to emergency dental care. The purpose of this study was to investigate the utilization of the virtual code by Canadian dentists during the beginning of the Covid-19 pandemic for children 18 years of age or younger. Methods: NIHB provided a secure data transfer portal for dental claims paid for the procedure “consultation with client for the emergency management and triage of client’s acute oral health care needs by phone or other virtual methods” and follow-up treatment with a date of service from April 1, 2020 to August 31, 2021. Data were analyzed using NCSS 2022 Statistical Software. Statistical analyses included frequencies, descriptive statistics, analysis of variance (ANOVA), and Chi-squared test. Results: Overall, 1040 patients 18 years of age or younger had a virtual visit with follow-up treatment between April 2020 and August 2021. The mean age was 10.5 ± 4.9 years and 54.3% were female. Most of the virtual consultations were completed by general practitioners 951 (91.4%) and pediatric dentists 86 (8.3%) and most initial virtual visits were completed in Quebec 358 (34.4%), Saskatchewan 266 (25.6%), Alberta 158 (15.2%), and Manitoba 120 (11.5%). More extractions were completed than any other type of dental treatment during the first visit following the initial consultation with a total of 417 extractions. The time between the initial consultation and the first follow-up visit was 0.9 1.9 months. Conclusions: Virtual visits were utilized during the Covid-19 pandemic for pediatric patients covered by NIHB and a greater uptake of virtual visits was seen in provinces with higher Registered First Nations and Inuit Peoples apart from Ontario. Teledentistry has the potential to be utilized for much more than dental emergencies to improve access to care, especially for remote and rural communities. Moving forward additional studies are needed to obtain parent/patient feedback on virtual consultations.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".