Designing an App for Immunosuppression Adherence and Communication: A Qualitative Approach
Bibliographic record
Abstract
BACKGROUND: Immunosuppression nonadherence may be the most important factor limiting long-term allograft survival. OBJECTIVE: Following user-centered design, we explored the essential priorities and preferences of kidney transplant recipients and healthcare providers (HCP) to inform development of a smartphone app to improve immunosuppression adherence and communication. DESIGN: A qualitative descriptive research design was used. SETTING: The University of Alberta Hospital adult kidney transplant program in Edmonton, Canada. PARTICIPANTS: Participants were recruited by convenience sampling and included 32 kidney transplant recipients and 11 HCPs. METHODS: Seven focus groups (5 with recipients and 2 with HCPs) were conducted to inform app development. Sessions were recorded, and transcripts were coded to elucidate themes. RESULTS: App development to improve adherence was not a priority for HCP. Recipients prioritized choice: that all features be optional. Recipients preferred support while traveling; access to laboratory results; and use by younger or newly transplanted recipients. Both recipients and HCP preferred linkage to pharmacy; and self-management and accountability.For the app to improve communication, HCPs believed the priorities to be addressed included: clarity on scope of app; legal, ethical, and professional obligations; and charting. Both recipients and HCP prioritized HCP workload, and broader medication and health concerns. Healthcare providers preferred tech support; both recipients and HCPs preferred app access for nontransplant HCP. LIMITATIONS: Limitations include underrepresentation of physicians, recipients with racial/ethnic diversity, and potential selection bias of transplant recipients who perceived themselves to be adhering to immunosuppression medications. CONCLUSION: Future research is needed for the app to become a comprehensive, secure platform for broader communication between recipients and HCP, pharmacies, and nontransplant clinicians while streamlining HCP workload.
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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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".