Relationship between Immunosuppressive Medications Adherence and Quality of Life and Some Patient Factors in Renal Transplant Patients in Iran
Bibliographic record
Abstract
BACKGROUND & AIM: About organ transplant, immunosuppressive medications adherence is a critical issue, because non-adherence to these medications causes rejection, reduces quality of life and increases treatment cost and mortality rate. Among these, the quality of life is deemed very important to evaluate treatment result and also it can be useful for discovering non adherence. The aim this study was to assess the relationship between medication adherence and quality of life and some patient factors in renal transplant patients. METHODS: The study was a descriptive-correlational design and was done on renal transplant patients over 18 who had undergone surgery for over 3 months, and were inclined to participate. Sample size was 230 people and sampling was convenience. Quality of life questionnaire in renal transplant patients and Immunosuppressant Therapy Adherence Scale were filled by patients and the data was analyzed by SPSS15 software. RESULTS: It showed that the mean score of quality of life in renal transplant patients was 21.65±4.03 and 57.8% of them did not adhere to immunosuppressive medications. Results of correlation between scores of immunosuppressive medication adherence and Quality of life showed that there were significant correlation in 3 dimensions of 4: health performance (p ≤ 0.0001 & rETA=0.23), social-economic (p=0.001 & rETA=0.15), psychological-spiritual (p=0.011 & rETA=0.15), also logistic test showed significant relationship between immunosuppressive medication adherence and number of transplantation (?=1.04, p= 0.048). CONCLUSION: According to the results, health care providers i.e. nurses must note to medication adherence as a health enhancement factor while treating and educating to these patients.
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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 | 0.001 | 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 | 0.001 |
| 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".