Predictors of Medication Adherence in Renal Transplant Patients: Self-Efficacy, Depressive Symptoms, and Cognition.
Bibliographic record
Abstract
Chronic Kidney disease (CKD) is relatively common among middle-aged and older adults. Just under 1000 people with CKD received kidney transplants in Canada in 2005, while three times that remained on waitlists. Studies report high rates of non-adherence to medications following renal transplant. The extent to which adherence is predicted by cognitive ability, depressive symptoms and self-efficacy, is an important issue in management of this illness. Research is needed to further understand how these variables are related to medication adherence in renal transplant patients. This project will examine the relationships between traditional and everyday measures of cognitive performance, general and medication adherence-specific self-efficacy, depressive symptoms, and medication adherence, in persons post renal transplant. Specifically, we are interested in how each of the aforementioned factors collectively contribute to medication adherence in these patients. We plan to use structural equation modeling techniques to statistically examine data collected in each of these domains, in hopes of better understanding the relationships between them.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 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".