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Dental anxiety as a hidden access barrier: prevalence, sociodemographic predictors, and service-utilization effects

2025· article· W4415622060 on OpenAlexaff
Abdulrahman Awad, Adam Friday

Bibliographic record

VenueJournal of AI-powered medical innovations. · 2025
Typearticle
Language
FieldDentistry
TopicDental Anxiety and Anesthesia Techniques
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsAnxietyMultidisciplinary approachMental healthOral healthStigma (botany)Dental careDental healthHealth equity

Abstract

fetched live from OpenAlex

Another major, yet significantly under-acknowledged cause of lower utilization of oral healthcare services is dental anxiety. Although there has been increased dental technology coupled with the focus on the patient, fear and anxiety are playing a role in ensuring that individuals do not get timely treatment resulting in the worsening of the oral health condition. This paper will cover the commonality of dental phobia, its sociodemographic predictors, as well as the effects of dental phobia on patterns of service uptake, especially in vulnerable groups. This study relies on a multidisciplinary literature review, thus showing how the age, gender, socioeconomic status, previous trauma, and mental health stigma augment the effects of dental fear. The article claims an oral health structural barrier is the hidden element of dental anxiety that transpires into greater oral health disparity by synthesizing international and regional data, especially with low- and middle-income situations. The results promote a universal culturally competent, and trauma-sensitive system of dental medicine, based on the trust in advance of screening and anxiety-reduction methods. Finally, the problem of dental anxiety is important not only to enhance oral health outcomes but to create equity in access to basic health services.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.325
Teacher spread0.312 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueJournal of AI-powered medical innovations.Same topicDental Anxiety and Anesthesia TechniquesFrench-language works237,207