Impact of the COVID‐19 pandemic on the University of British Columbia Geriatric Dentistry Program: Clinical education and service
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: In Canada, the COVID-19 pandemic was associated with significant morbidity and mortality in older adults, particularly those in long-term care (LTC). Access to oral health services was limited during the pandemic due to public health restrictions. The aim of this paper was to describe the impact of the pandemic on the clinical education and service of the University of British Columbia (UBC) Geriatric Dentistry Program (GDP), which provides care to LTC residents. METHODS: Data were collected from UBC GDP AxiUm dental software records, including number of dental appointments in 2019 and 2020. Data on revenue in 2019 and 2020 based on clinical production were collected through financial summary reports. Data on the number of educational rotations were collected from summary reports from scheduling software. RESULTS: In 2020, significant reductions in clinical service, revenue, and productivity were observed in the UBC GDP relative to 2019. The number of GDP appointments for June-December 2020 was lower by 68%. The clinical productivity reduced by 67% for the same period. Expenses were slightly reduced. The overall number of LTC clinical rotations for students were only slightly lower for undergraduate students in 2020 than in 2019, and it increased for graduate students. CONCLUSION: The COVID-19 pandemic and associated public health restrictions had a negative impact on the clinical service and productivity of the UBC GDP in 2020 relative to 2019. However, clinical educational rotations to LTC were slightly increased in 2020 relative to 2019. Dental care for LTC residents can be provided if rigorous administrative controls, engineering controls and personal protective equipment are employed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".