An observational study of health literacy and medication adherence in adult kidney transplant recipients
Bibliographic record
Abstract
BACKGROUND: There is a high prevalence of non-adherence to immunosuppressants in kidney transplant recipients. Although limited health literacy is common in kidney recipients and is linked to adverse outcomes in other medical populations, its effect on medication adherence in kidney transplant recipients remains poorly understood. The objective was to investigate the effect of lower health literacy on immunosuppressant adherence. METHODS: Kidney recipients who were at least 6 months post-transplant and outpatients of Vancouver General Hospital in B.C., Canada were recruited through invitation letters. A total of 96 recipients completed the Health Literacy Questionnaire, which provides a multifactorial profile of self-reported health literacy and the Transplant Effects Questionnaire-Adherence subscale measuring self-reported immunosuppressant adherence. Hierarchical linear regression was used to analyze the association between health literacy and adherence after controlling for identified risk factors of non-adherence. RESULTS: = 0.08, P = 0.004) and lower scores on six of nine of the health literacy factors. CONCLUSIONS: Poorer health literacy is associated with lower immunosuppressant adherence in adult kidney transplant recipients suggesting the importance of considering a recipient's level of health literacy in research and clinical contexts. Medication adherence interventions can target the six factors of health literacy identified as being risk factors for lower medication adherence.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".