Resilience and therapeutic regimen compliance in patients undergoing hemodialysis in hospitals of Hamedan, Iran
Bibliographic record
Abstract
BACKGROUND: The relationship between resilience, one of the important issues in mental health, and therapeutic regimen compliance, the key in the successful management of chronic disease such as chronic kidney disease, is unclear. OBJECTIVE: The aim of this study was to determine the relationship between resilience and therapeutic regimen compliance in ESRD patients, undergoing hemodialysis. METHODS: In this cross-sectional study, 107 hemodialysis patients referred to Besat and Shahid Beheshti Hospitals of Hamedan, Iran were selected through systematic sampling method from January to April 2013. The tool was Connor-Davidson Resilience Scale (CD-RISC). The criteria for compliance were: mean of interdialytic weight gain less than 5.7% to the dry weight, serum potassium 5.5 mEq/L or less, serum phosphorus of 6 mg/dL or less and no more than 3 absences in dialysis sessions. For data analysis, statistical tests such as independent t-test and Logistic regression were performed. RESULTS: Fifty-seven (53.3%) patients were males and the mean age of subjects was 49.96±17.39 years. The difference in the mean scores of resilience between compliance or non-compliance patients was statistically significant (p=0.032). Only resilience and age were significant factors related to regimen compliance. In those subjects with greater resilience for 1 score, the chance of compliance with the therapeutic regimen would be 5.4% higher (OR=1.054, CI 95%: 1.01-1.103). In addition, the elderly patients were more likely to comply with the regimen (OR=1.072, CI 95 %: 1.033, 1.113). CONCLUSION: According to the results, the patients with greater scores of resilience were more likely to comply with the therapeutic regimen.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".