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.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".