Dental anxiety as a hidden access barrier: prevalence, sociodemographic predictors, and service-utilization effects
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".