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Record W4310680070 · doi:10.3390/ijerph192316216

Psychological Impact of COVID-19 in the Setting of Dentistry: A Review Article

2022· review· en· W4310680070 on OpenAlexaboutno aff
Juan Carlos De Haro, Eva Rosel, Inmaculada Salcedo‐Bellido, Ester Leno‐Durán, Pilar Requena, Rocío Barrios‐Rodríguez

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2022
Typereview
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsnot available
Fundersnot available
KeywordsScopusCoronavirus disease 2019 (COVID-19)MEDLINEPandemicAnxietyWeb of scienceMedicine2019-20 coronavirus outbreakHealth professionalsFamily medicinePsychologyHealth careMeta-analysisPsychiatryPathologyPolitical scienceDisease

Abstract

fetched live from OpenAlex

The worldwide pandemic has exposed healthcare professionals to a high risk of infection, exacerbating the situation of uncertainty caused by COVID-19. The objective of this review was to evaluate the psychological impact of the COVID-19 pandemic on dental professionals and their patients. A literature review was conducted using Medline-Pubmed, Web of Science, and Scopus databases, excluding systematic reviews, narratives, meta-analyses, case reports, book chapters, short communications, and congress papers. A modified version of the Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the selected studies. The search retrieved 3879 articles, and 123 of these were selected for the review (7 longitudinal and 116 cross-sectional studies). Elevated anxiety levels were observed in dental professionals, especially in younger and female professionals. Except for orthodontic treatments, patients reported a high level of fear that reduced their demand for dentist treatment to emergency cases alone. The results suggest that the COVID-19 pandemic has had psychological and emotional consequences for dental professionals and their patients. Further research is necessary to evaluate the persistence of this problem over time.

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)0.013
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.965
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.327
GPT teacher head0.586
Teacher spread0.260 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public HealthSame topicDental Research and COVID-19French-language works237,207