Web-Based Tailored Nursing Intervention to Support Medication Self-management
Bibliographic record
Abstract
Optimal adherence to immunosuppressive medication is essential to kidney graft success. A Web-based tailored virtual nursing intervention was developed to promote medication adherence and support self-management among kidney transplant recipients. A qualitative study was undertaken in a hospital setting in Montreal (Canada) to document how users experience the intervention and to explore medication intake self-management behaviors. To participate, transplant recipients had to be at least 18 years old and had to have completed at least one computer session of the intervention. Semistructured interviews were conducted with 10 participants (two women, eight men) with a mean age of 47.8 years. They reported receiving their latest renal transplant on average 10.6 years prior. Content analysis of the interview transcripts yielded five major themes: (1) kidney transplant is a gift from life; (2) routinization of medication intake; (3) intervention is a new and positive experience; (4) using the intervention offers many benefits; and (5) individual relevance of the intervention. Patient experience shows the intervention is acceptable and can help better manage medication intake. Results also underscore the importance of offering the intervention early in the care trajectory of transplant recipients. Web-based tailored virtual nursing interventions could constitute an easily available adjunct to existing specialized services.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".