Status and correlates of alexithymia among Chinese empty nest elders in a cross-sectional study
Bibliographic record
Abstract
Alexithymia is regarded as a 'lack of words for emotions' and is a subclinical trait. Empty-nest elderly in China are at higher risk of developing alexithymia due to the nature of their lives. Alexithymia can cause further damage to the mental health of empty nesters and also affect recovery from illness. Nevertheless, studies on alexithymia among Chinese empty-nesters are scarce, particularly in terms of large-scale empirical research. This study aims to assess the prevalence of alexithymia among the Chinese empty-nest elderly and to determine potential factors associated with alexithymia. A cross-sectional design was used. A total of 394 empty-nest elderly (195 males and 199 females) in Chengdu City and Ziyang City were selected by convenience sampling. Among them, individuals aged 60-69 years, 70-79 years, and ≥ 80 years old accounted for 50.5%, 31.5%, and 18.0%, respectively. All participants were surveyed from April 2022 to June 2023. The total incidence of alexithymia and borderline alexithymia among participants was 33.5% and 35.3% in this study. The personality (introversion and ambiversion), number of chronic diseases (2-3 and ≥ 4), social loneliness, and emotional loneliness were positively associated with alexithymia (p < 0.01), while instrumental support, emotional support, provided support, education level (bachelor degree or higher), occupation before retirement (worker and public institution personnel) and monthly income [1001-3000 Chinese yuan (CNY; ¥; RMB) and ≤ 1000 CNY] were negative factors (p < 0.01); these factors explained 55.3% of the total model variance. Our findings suggested that empty nesters' alexithymia appears to be influenced by many factors, especially social support and loneliness. The results may provide clues to help health professionals identify empty-nesters' vulnerability to alexithymia and reduce its prevalence.
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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 | 0.001 | 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".