Alexithymia in patients with type 2 diabetes mellitus: the role of anxiety, depression, and glycemic control
Bibliographic record
Abstract
OBJECTIVE: This study was aimed at determining the prevalence of alexithymia in patients with type 2 DM and the factors affecting it. METHODS: This cross-sectional study was conducted with 326 patients with type 2 DM. Study data were collected with the Personal Information Form, Toronto Alexithymia Scale, and Hospital Anxiety and Depression Scale. Glycemic control was assessed by glycated haemoglobin (HbA1c) results. The analysis was performed using descriptive statistics, chi-square test, Pear-son's correlation, and logistic regression analysis. RESULTS: Of the patients, 37.7% were determined to have alexithymia. A significant relationship was determined between alexithymia and HbA1c, depression, and anxiety. According to binary logistic regression analyses, alexithymia was 2.63 times higher among those who were in a paid employment than those who were not, 2.09 times higher among those whose HbA1c levels were ≥7.0% than those whose HbA1c levels were <7.0%, 3.77 times higher among those whose anxiety subscale scores were ≥11 than those whose anxiety subscale scores were ≤10, and 2.57 times higher among those whose depression subscale scores were ≥8 than those whose depression subscale scores were ≤7. CONCLUSION: In this study, it was determined that two out of every five patients with DM had alexithymia. Therefore, their treatment should be arranged to include mental health care services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".