Medication adherence in renal transplant recipients: A latent variable model of psychosocial and neurocognitive predictors
Bibliographic record
Abstract
OBJECTIVE: Estimates indicate that 20-70% of renal transplant recipients are medication non-adherent, significantly increasing the risk of organ rejection. Medication adherence is negatively impacted by lower everyday problem solving ability, and associations between depressive symptoms, self-efficacy, and adherence are reported in renal transplant recipients. Nonetheless, to date, these associations have not been examined concurrently. Given the relationship between non-adherence and organ rejection, it is critical to gain a better understanding of the predictors of adherence in renal transplant recipients. To this end, we modeled relationships among cognitive abilities, depressive symptoms, self-efficacy, and adherence in this group. METHODS: Participants (N = 211) underwent renal transplant at least one year prior to participation. Adherence was measured via self-report, medication possession ratio, and immunosuppressant blood-level. Traditionally-measured neurocognitive and everyday problem-solving abilities were assessed. Depressive symptoms were measured via self-report, as were general and medication adherence related self-efficacy. Structural equation modeling was used to assess the fit of the model to available data. RESULTS: Everyday problem solving and self-efficacy had direct positive associations with adherence. Depressive symptoms were negatively associated with self-efficacy, but not adherence. Traditionally-measured neurocognitive abilities were positively associated with self-efficacy, and negatively associated with depressive symptoms. CONCLUSIONS: We present a comprehensive investigation of relationships between cognitive and psychosocial factors and adherence in medically stable renal transplant recipients. Findings confirm the importance of everyday problem solving and self-efficacy in predicting adherence and suggest that influences of depressive symptoms and neurocognitive abilities are indirect. Findings have important implications for future development of interventions to improve medication adherence in renal transplant recipients.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".