Psychological Distress in Hemodialysis: Impact of Life Events, Illness Perception, and Difficulty Processing Emotions (Alexithymia)
Bibliographic record
Abstract
BACKGROUND: Depression and anxiety in individuals undergoing hemodialysis (HD) significantly impair daily functioning and hinder renal rehabilitation efforts. Various factors, including negative life events, illness perceptions, and difficulty processing emotions (alexithymia), have been associated with these psychological challenges; however, their specific impact on HD-related psychological distress remains unclear. METHODS: In this cross-sectional study, 246 individuals receiving HD were assessed using self-administered questionnaires, including the Life Events Scale (LES), Illness Perceptions Questionnaire (IPQ-R), Toronto Alexithymia Scale (TAS-20), and the Hospital Anxiety and Depression Scale (HADS). RESULTS: The study found that 32.5% and 41.5% of HD patients exhibited symptoms of depression and anxiety, respectively. Logistic regression analyses revealed significant correlations between negative life events, illness perceptions, and alexithymia with both depression and anxiety. Specifically, higher scores on the "Negative Emotional Representation about Illness" subscale were associated with an increased risk of depression (adjusted odds ratio [OR], 1.302; 95% confidence interval [CI], 1.118-1.544; p < 0.001). Conversely, lower scores on "Personal Control" were linked to a heightened risk of depression (adjusted OR, 0.796; 95% CI, 0.683-0.927; p = 0.003). For anxiety, elevated scores in "Negative Emotional Representation about Illness" (adjusted OR, 1.185; 95% CI, 1.014-1.261; p = 0.015) and "difficulty identifying feelings" (adjusted OR, 1.210; 95% CI, 1.031-1.411; p = 0.016) indicated increased risk, while lower scores in "Personal Control" were similarly associated with heightened anxiety risk (adjusted OR, 0.852; 95% CI, 0.734-0.983; p = 0.042). CONCLUSION: This study suggests that negative life events, specific illness perceptions, and alexithymia are significant predictors of depression and anxiety among HD patients. Addressing maladaptive illness perceptions and emotional regulation deficits could offer novel strategies to enhance mental health outcomes in this population, highlighting the need for further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".