Difficulties in emotional regulation: association with poorer oral health‐related quality of life in the general population
Bibliographic record
Abstract
Personality is one of the strongest predictors of subjective well-being and may, according to a few previous studies, affect how people report oral health-related quality of life (OHRQoL). Alexithymia, a personality trait involving difficulties in emotional regulation, is associated with poorer health-related quality of life in the general population. We studied if alexithymia is also associated with poorer OHRQoL in a general population sample of 4,460 adults. Oral health-related quality of life was measured using the 14-item Oral Health Impact Profile (OHIP-14) and alexithymia was measured using the 20-item Toronto Alexithymia Scale (TAS-20). Controlling for clinically assessed dental health, depression, anxiety, and socio-demographic variables, higher scores on the TAS-20 as well as on its three dimensions [difficulties in identifying feelings (DIF), difficulties in describing feelings (DDF), and externally oriented thinking (EOT)] were associated with higher OHIP-14 composite scores according to Poisson regression analyses. In adjusted logistic regression analyses, the TAS-20 and two of its dimensions (DIF and DDF) were positively and significantly associated with the seven OHIP-14 dimensions and the prevalence of those reporting one or more OHIP-14 items fairly often or very often. The study showed that difficulties in emotional regulation might be reflected in poorer OHRQoL, regardless of the dental health status, depression, anxiety, and socio-demographic variables.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".