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Record W4200482818 · doi:10.3390/ijerph19010031

Reviewing Teledentistry Usage in Canada during COVID-19 to Determine Possible Future Opportunities

2021· review· en· W4200482818 on OpenAlexafffundabout
Sonica Singhal, S. Mohapatra, Carlos Quiñonez

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2021
Typereview
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsWestern UniversityPublic Health OntarioUniversity of Toronto
FundersFaculty of Dentistry, University of TorontoUniversity of Toronto
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BetacoronavirusMEDLINEPandemicCoronavirus InfectionsMedical emergencyBusinessEnvironmental healthMedicineVirologyPolitical scienceOutbreak

Abstract

fetched live from OpenAlex

During the COVID-19 pandemic, the limited in-person availability of oral health care providers resulted in an unprecedented utilization of the teledentistry tool. This paper reviews how Canadian organizations supported teledentistry and what can be expected about its usage in the post-pandemic era. An environmental scan across relevant Canadian federal, provincial, and territorial organizations was conducted to review pertinent publicly available documents, including dental regulators' or associations' COVID-19 guidance documents, government documents, and media articles. Almost all jurisdictions promoted teledentistry for triaging dental emergencies and screening patients for COVID-19 symptoms but not even half of them have developed guidelines in terms of modalities of usage, handling of personal information, informed consent process, or maintaining standards of practice. During the COVID-19 recovery phase, these advances across Canada will support in developing a comprehensive guidance for teledentistry and possibly specific codes for its utilization. This can create a niche for teledentistry as an adjunct to the main stream dental care delivery where some visits can always be accommodated virtually, reducing disparities in oral healthcare between rural and urban communities. Ultimately, this can potentially make oral health care delivery more effective, efficient, and environmentally friendly in Canada.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.269
GPT teacher head0.467
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations30
Published2021
Admission routes3
Has abstractyes

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