Why do Patients Forget to Take Immunosuppression Medications and Miss Appointments: Can a Mobile Phone App Help?
Bibliographic record
Abstract
BACKGROUND: Kidney transplant recipients must adhere to their immunosuppressive medication regimen. However, non-adherence remains a major problem. OBJECTIVE: The aim of this paper is to determine how kidney transplant recipients remember to take their medications, and assess their perception and beliefs about adherence to immunosuppressive medications and barriers to medication adherence. In addition, we aim to assess perception and beliefs about willingness to use a hypothetical, mobile phone app to improve adherence. METHODS: We conducted a qualitative study that included an average of three home or workplace visits of kidney transplant recipients (N=16) from a single urban transplant center. RESULTS: The qualitative study revealed that transplant recipients understood the importance of taking their immunosuppressive medications and this motivated them to take their medications. The visits showed that most participants have incorporated medication use into their daily lives and that any minor deviation from daily routines could result in non-adherence. Participants also reported other barriers to adherence. All participants were interested in using an app to remind them to take their medication; however, they reported potential barriers to using the app. CONCLUSIONS: Although kidney transplant recipients understood the importance of medication adherence, there were significant barriers to maintaining adherence. Participants also reported interest in using a mobile phone app.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".