The mediating effect of alexithymia in the symptom burden and depression in patients with maintenance hemodialysis
Bibliographic record
Abstract
Aim This study aimed to investigate the prevalence of alexithymia among patients receiving maintenance haemodialysis (MHD) and whether it plays a role in the relationship between symptom burden and depression in this population. Background The prevalence of depression among patients undergoing MHD is increasing. Numerous studies have found strong links between alexithymia, symptom burden, and the development of depression in this population. However, the underlying mechanisms and alexithymia's specific mediating role in the relationship between symptom burden and depression are poorly understood and have received little attention in the existing literature. Methods This study included 380 MHD patients in a haemodialysis center, with a mean age of 58.98 ± 13.86 years, using a self-designed patient general information questionnaire, disease-related information questionnaire, dialysis patient symptom burden scale, depression scale, and Toronto Alexithymia Scale (TAS-20). A regression model of the factors influencing depression was developed using structural equation modeling. Results MHD patients had a DFSSBI score of 77.41 ± 45.74, a TAS-20 score of 55.36 ± 11.17, and a Patient Health Questionnaire (PHQ-9) score of 6.07 ± 4.60. The burden of symptoms was positively correlated with alexithymia and depression (r = 0.367, 0.776, P = 0.000). The regression model had a high goodness of fit (χ2/df = 1.604, RMSEA = 0.040, GFI = 0.986, CFI = 0.999, TLI =0.997). The structural equation model analysis found the following: symptom burden was a positive predictor of alexithymia, β = 0.296, P < 0.001; alexithymia was a positive predictor of depression, β = 0.752, P < 0.001; and symptom burden was a positive predictor of depression, β = 0.141, P < 0.001. Conclusion The level of depression in MHD patients is closely related to the burden of symptoms and alexithymia, with alexithymia serving as a partial intermediary between the two. Addressing the emotional wellbeing and symptom load of MHD patients is critical to relieving their depressive symptoms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".