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.
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 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 | 0.000 |
| 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) | 0.000 | 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".