Structural equation modeling analysis of factors influencing decisional conflict between dialysis modality among end-stage kidney disease patients in Wuhan
Bibliographic record
Abstract
OBJECTIVES: To explore the influencing factors and relationships associated with decisional conflict of dialysis modality in End-stage kidney disease (ESKD) patients. METHODS: This study was a survey-based cross-sectional investigation conducted on 150 ESKD patients in a third-class hospital in Wuhan. The general information questionnaire, decisional conflict scale, Montreal cognitive assessment, frail scale, perceived social support scale, and brief health literacy screen were used for investigation. SPSS 25.0 was used to compare the differences between the decisional and non-decisional conflict groups, and AMOS 23.0 was used to construct a structural equation model to explore the influencing factors. RESULTS: The incidence of decisional conflict in 150 ESKD patients was 33.3% (50/150). Binary logistic regression analysis showed that the independent risk factors for decisional conflict of dialysis modality in ESKD patients included monthly household income (OR = 0.184), cognitive function (OR = 7.0), social support (OR = 0.891), health literacy (OR = 0.608), the level of eGFR (OR = 1.488), and the level of cTnI (OR = 9.558). The constructed path analysis model had a good fit (x2/df = 1.499, GFI = 0.957, AGFI = 0.911, NFI = 0.906, CFI = 0.967, RMSEA = 0.055). The path analysis showed that health literacy (0.577) had the greatest impact on the decisional conflict, with a direct effect of 0.480 and an indirect effect of 0.097 through cognitive function and monthly household income. Next was social support, with an effect value of 0.434. CONCLUSIONS: In clinical practice, it is important to enhance the health literacy of patients and their families and to provide advanced education on dialysis plans. Additionally, in managing and planning chronic kidney disease progression and dialysis, it is recommended to regularly and systematically assess cognitive function, particularly before the patient's cognitive impairment worsens or the severity of the disease progresses. Advanced care planning can be established through collaboration between healthcare professionals and patients to ensure appropriate decision-making and management. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: This paper finds that the factors that influence and relate to dialysis methods in end-stage renal disease patients help nurses exercise autonomy better, assist patients in reducing their decisional conflict, and improve clinical outcomes. PATIENT OR PUBLIC CONTRIBUTION: Patients received a relevant questionnaire survey, and caregivers assisted in conducting the study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".