Exploring the association between oral health literacy and alexithymia.
Bibliographic record
Abstract
UNLABELLED: Low health literacy and alexithymia have separately been emphasized as barriers to patient-practitioner communication, but the association between the two concepts has not been explored. OBJECTIVE: To test the hypothesis that low oral health literacy and alexithymia are associated. METHOD: Adults (n=127) aged 21-80 years (56% women) participated in this cross-sectional study. Oral health literacy was assessed using the interview-based Adult Health Literacy Instrument for Dentistry (AHLID) with scores from 1-5. The self-administered Toronto Alexithymia Scale (TAS-20) was used to assess three distinct TAS-20 factors and TAS-20 total score. RESULTS: Significant negative correlations between AHLID scores and TAS-20 factors 2, 3 and TAS-20 total score were found. Regression analyses showed that TAS-20 factor 3, externally-oriented thinking (β=-0.21, SE=0.02, p=0.017), and TAS-20 total score (β=-0.18, SE=0.01, p=0.036) were significant predictors of AHLID level. CONCLUSION: The hypothesis that low oral health literacy is associated with alexithymia was supported. This finding proposes that alexithymia may be considered as a possible factor for low oral health literacy. However, the correlations are not strong, and the results should be regarded as a first step to provide evidence with additional research on this topic being needed.
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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 | 0.004 |
| 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 | 0.001 | 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".