The effect of alexithymia on self-perceived aging among community-dwelling older adults with multiple chronic conditions: the mediating role of maladaptive cognitive emotion regulation strategies
Bibliographic record
Abstract
Objectives: This study investigated the mediating role of maladaptive cognitive emotion regulation strategies in the relationship between alexithymia and self-perceived aging among older adults. Methods: We surveyed 478 Chinese community-dwelling older adults from November 2022 to May 2023. The Toronto Alexithymia Scale (TAS-20), Cognitive Emotion Regulation Questionnaire (CERQ), and Brief Aging Perceptions Questionnaire (B-APQ) were used. Correlation analyses, multiple linear regression analysis, and structural equation modeling were performed. Results: The average age of participants was 71.52 ± 7.80 years, and the number of chronic diseases was distributed as follows: 270 (56.49%) had 2 disease conditions, 156 (32.64%) had 3 disease conditions, and 52 (10.88%) had 4 disease conditions or more. Scores for alexithymia 57.83 ± 10.19; maladaptive cognitive emotion regulation strategies 49.63 ± 10.65; and self-perceived aging 58.74 ± 10.23. Alexithymia and maladaptive cognitive emotion regulation strategies were positively correlated with negative self-perceived aging (r=0.665 and r=0.673, respectively), explaining 51.8% of the variance in self-perceived aging. Structural equation modeling results showed that alexithymia had a direct effect (of 0.368) on self-perceived aging, accounting for 44.1% of the total effect. Maladaptive cognitive emotion regulation strategies partially mediated the relationship, with a mediation effect of 0.386, accounting for 51.8% of the total effect. Conclusion: These findings suggest that addressing maladaptive cognitive emotion regulation strategies can help reduce negative self-perceived aging in elderly individuals with multiple chronic conditions, particularly among those with alexithymia.
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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 | 0.001 | 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) | 0.001 | 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".