Peer Mentoring as an Avenue to Explore in Kidney Transplantation: Kidney Transplant Recipients’ Perspectives on Peer Mentoring
Bibliographic record
Abstract
Kidney transplant recipients (KTRs) face numerous challenges following transplantation. Given that healthcare professionals do not necessarily have first-hand experience living with or waiting for a transplant, it may be hard for them to communicate with and help patients when it comes to transplant-related issues. KTRs have experiential knowledge, which could enable them to help other patients going through similar situations. Peer mentoring involves pairing mentees with individuals who have had similar experiences, to provide training, information, and emotional support. The aim of this study was to gather KTRs' perspectives on a potential peer mentoring program. METHODS: A total of 13 KTRs took part in focus groups between June 2017 and March 2018. The focus group discussions were digitally recorded and transcribed. The content of the interviews was analyzed using the qualitative data analysis method, including deductive and inductive coding techniques. RESULTS: All participants were supportive of a peer mentoring program as it is a convenient and user-friendly way to address posttransplant concerns. Peer mentoring was also viewed as a way to communicate with other patients who have gone through similar experiences and relieve the loneliness and isolation some participants experienced subsequent to their transplant. Young female participants expressed a need to connect with other young, female transplant recipients to be able to discuss issues such as pregnancy, sexuality, and body image. CONCLUSIONS: KTRs who participated in this study expressed a need for the development of a peer mentoring program to help other KTRs face the medical (including cardiovascular disease), social, and psychological challenges associated with kidney transplantation. The results of this study are the first step in the development of a structured peer mentoring program at the kidney transplantation clinic.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".