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.
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 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".