Associations of sensory sensitivity, pain catastrophizing, and alexithymia with dental anxiety
Bibliographic record
Abstract
We aimed to reveal interrelationships between alexithymia, catastrophic thinking, sensory processing patterns, and dental anxiety among 460 participants who were registrants of a Japanese research company. Measures used were the Modified Dental Anxiety Scale, the Adult Sensory Profile, the Pain Catastrophizing Scale, and the 20-item Toronto Alexithymia Scale. The interrelationships among the constructs were analyzed using structural equation modeling, adjusting for age, gender, and negative dental treatment experience. Data from 428 participants were used in the analyses. Sensory sensitivity and pain catastrophizing were independently associated with anticipatory and treatment-related dental anxiety, while difficulty identifying feelings was not. In the mediation model, sensory sensitivity and pain catastrophizing served as full mediators between difficulty identifying feelings and the dimensions of dental anxiety (indirect effects were between 0.13 and 0.15). The strength of the associations was 0.55 from difficulty identifying feelings to both pain catastrophizing and sensory sensitivity, and between 0.24 and 0.26 to anticipatory and treatment-related dental anxiety. The association between trait-like phenomena, such as alexithymia, and dental anxiety may be mediated by neurophysiological and cognitive factors such as sensory sensitivity and pain catastrophizing. These findings could be crucial for new and innovative interventions for managing dental anxiety.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".