Psychosocial Predictors of Bruxism
Bibliographic record
Abstract
OBJECTIVES: The study aimed to investigate the psychosocial predictors of bruxism. The association of various psychosocial factors such as alexithymia, emotional processing, state and trait anxiety, and stress with awake bruxism was analysed. METHODS: The study involved 52 volunteers diagnosed with awake bruxism. The toolkit that was used included the Toronto Alexithymia Scale (TAS-20), the Emotional Processing Scale (EPS), the Cohen Perceived Stress Scale (PSS-10), and the State- and Trait-Anxiety Inventory (STAI), with independent individual psychological diagnoses being made for every patient. The results were statistically analysed using IBM SPSS Statistics 24. RESULTS: The obtained data clearly show that psychological traits-both permanent dispositions (e.g., state anxiety and alexithymia) and temporary states (e.g., trait anxiety, emotional processing deficits, and psychological stress)-are significant determinants of awake bruxism. The percentage of explained variance indicates the presence of other factors as well. CONCLUSIONS: Psychosocial factors such as state anxiety and trait anxiety, alexithymia, and perceived stress are as important as somatic causes in the occurrence and maintenance of awake bruxism. The profile of the obtained data suggests the possibility of preventing or minimizing the symptoms of awake bruxism through properly constructed psychoprophylactic interactions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".