Relationship Between Illness Adaptation and Type D Personality in Hemodialysis Patients
Bibliographic record
Abstract
INTRODUCTION: Although the impact of Type D personality on individuals with various chronic health conditions has been widely studied, its role in hemodialysis patients remains unclear. This study aimed to explore the relationship between illness adaptation and Type D personality in hemodialysis patients and to examine the sociodemographic and clinical characteristics that influence illness adaptation. METHODS: This cross-sectional study was conducted in hemodialysis units in western Turkey from November 2021 to November 2022. Data were collected using a Personal Information Form, the Adaptation to Chronic Illness Scale, and the Type D Personality Scale. Descriptive statistics, t-tests, ANOVA, Pearson's correlation, and multivariate linear regression analysis were applied to analyze the data. RESULTS: A total of 138 hemodialysis patients participated in the study. The mean score for adaptation to chronic illness was 80.6 ± 13.6. Among these patients, 38.4% had Type D personality. A negative correlation was found between illness adaptation and Type D personality (r = -0.683, p < 0.001). Regression analysis revealed that Type D personality (β = -0.255), gender (β = -0.087), age (β = 0.137), education level (β = 0.152), economic status (β = 0.177), and hemodialysis duration (β = 0.307) were significant predictors of illness adaptation. CONCLUSION: Healthcare providers should offer individualized psychosocial care and counseling services to enhance illness adaptation in hemodialysis patients. In particular, strategies to improve adaptation should be tailored for patients with Type D personality traits.
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 | 0.000 | 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 | 0.000 |
| 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".