Predicting medication adherence and employment status following kidney transplant: The relative utility of traditional and everyday cognitive approaches.
Bibliographic record
Abstract
OBJECTIVE: The authors investigated the utility of both traditional and everyday cognitive measures in predicting medication adherence and employment status among kidney transplant recipients. In addition, the role of noncognitive predictors was examined. METHOD: Cognitive measures of processing speed, memory, everyday problem solving, executive functioning, and questionnaires assessing mood, medication adherence, and employment status were individually administered to 108 kidney transplant recipients. Because the eligibility criteria differed for the two analyses, there were 103 participants in the medication adherence analyses and 94 participants in the employment analyses. Stepwise hierarchical regression and sequential binomial logistic regression analyses were conducted for continuous and dichotomous outcome measures, respectively. RESULTS: Findings indicate that both poorer performance on the everyday problem-solving test and a higher number of depressive symptoms were predictive of poorer self-reported medication adherence (R(2) = .19, p < .01). Furthermore, being on antidepressant medication, having a higher number of depressive symptoms, and poorer performance on traditional neuropsychological measures were predictive of fewer hours worked (Nagelkerke's R(2) = .29, ps <.05). CONCLUSIONS: This study highlights the differential associations between neurocognitive and psychosocial status, and medication adherence and employment status following kidney transplantation. The findings suggest that the relative importance of traditional and everyday measures is dependent upon the outcome examined.
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 | 0.000 | 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".