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Record W3160319599

Dentists' Experiences and Dental Care in the COVID-19 Pandemic: Insights from Nova Scotia, Canada.

2021· article· en· W3160319599 on OpenAlexaboutno aff
Nioushah Noushi, Afisu A. Oladega, Michael Glogauer, David Chvartszaid, Christophe Bedos, Paul Allison

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaPandemicDental careHealth careFamily medicineCoronavirus disease 2019 (COVID-19)NursingMedicinePsychologyPolitical scienceSociologyDiseaseInfectious disease (medical specialty)
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: This study aimed to describe dental care provision and the perceptions of dentists in Nova Scotia, Canada, during 1 week of the COVID-19 pandemic, shortly after the closing down of non-emergency, in-person care. METHODS: A survey was distributed to all 542 registered dentists in Nova Scotia, asking about dental care provision during 19-25 April 2020. Most answers were categorical, and descriptive analyses of these were performed. Data from the 1 open-ended question were analyzed using an inductive approach to identify themes. RESULTS: The response rate was 43% (n = 235). Most dentists (181) provided care but only 13 provided in-person care. From the open-ended question, 4 concerns emerged: communication from the regulatory authority; respondents' health and that of their staff; the health of and access to care for patients; and the future of their business. CONCLUSION: Most respondents remained engaged in non-in-person dental care using various modes. They expressed concerns about their health and that of their staff and patients as well as about the future of their practice. PRACTICAL IMPLICATIONS: Dentists and dental regulatory authorities should engage in discussions to promote the health of dental staff and patients and quality of care during the chronic phase of the pandemic.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.309
Teacher spread0.259 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2021
Admission routes1
Has abstractyes

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