Factors impacting the medication “Adherence Landscape” for transplant patients
Bibliographic record
Abstract
BACKGROUND: Medication non-adherence contributes to post-transplant graft rejection and failure; however, limited knowledge about the reasons for non-adherence hinders the development of interventions to improve adherence. We conducted focus groups with solid organ transplant recipients regarding overlooked challenges in the process of transplant medication self-management and examined their adherence strategies and perceptions towards the post-transplant medication regimen. METHODS: We conducted four focus groups with n = 31 total adult transplant recipients. Participants had received kidney, liver, or combined liver/kidney transplant at Johns Hopkins Hospital between 2014 and 2019. Focus groups were audio-recorded and transcribed. Transcripts were analyzed inductively, using the constant comparative method. RESULTS: Responses generally fell into two major categories: (1) barriers to adherence and (2) "adherence landscape". We define the former as factors directly labeled as barriers to adherence by participants and the latter as factors that heavily influence the post-transplant medication self-management process. CONCLUSIONS: We propose a shift in the way healthcare providers and researchers, address the question of medication non-adherence. Rather than asking why patients are non-adherent, we suggest that constructing and understanding patients' "adherence landscape" will provide an optimal way to align the goals of patients and providers and boost health outcomes.
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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".