Education Before Kidney Transplantation
Bibliographic record
Abstract
CONTEXT: Poor knowledge about immunosuppressive (IS) medications remains a major problem for patients in the posttransplant setting. Therefore, more effective educational strategies in the pretransplant setting are being considered as a possible method to improve knowledge and readiness for the challenges of posttransplant care. However, the most effective/relevant content of a pretransplant educational program is yet to be determined. OBJECTIVE: To identify pretransplant education topics from the posttransplant patient perspective. DESIGN: A focus group meeting was conducted among 7 high-functioning, stable adult kidney transplant recipients recruited from the Saskatchewan Transplant Program. Demographic information including age, gender, occupation, background/ethnicity, and time since transplant were recorded. A moderator, assistant moderator, and research assistant facilitated the 90-minute focus group meeting using a predetermined semistructured interview guide. The session was audio recorded and transcribed verbatim. Nvivo software was used to code the data and identify emerging themes exploring views of participants relating to the educational information required for pretransplant patients. RESULTS: Patients were satisfied with the education they had received. Ideas were classified into the following major themes-patient satisfaction, transplant waitlist, surgery, medications, posttransplant complications, lifestyle and monitoring, knowledge acquisition, illusion of control, and life changes posttransplant. Knowledge gaps were identified in all areas of the transplantation process and were not exclusive to IS medications. CONCLUSION: Misconceptions regarding transplantation were identified by a group of high-functioning, stable adult recipients who were satisfied with their clinical care. Future educational strategies should aim to address the entire transplantation process and not be limited to medications.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.006 |
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".