Living Kidney Donation Stories and Advice Shared Through a Digital Storytelling Library: A Qualitative Thematic Analysis
Bibliographic record
Abstract
Rationale & Objective: Despite the development of numerous educational interventions, there has been limited change in actual living donor kidney transplant (LDKT) rates over time. New strategies, such as the inclusion of patient stories in patient education, show promise to inspire more people to donate kidneys. This study identified the challenges faced, coping strategies used, and advice shared by transplant donors and recipients. Study Design: Qualitative thematic analysis. Setting & Participants: One hundred eighteen storytellers across the United States and Canada, including 82 living donors and 36 kidney recipients of living donor transplants who shared their stories on the Living Donation Storytelling Project (explorelivingdonation.org), an online digital storytelling platform and library. Analytical Approach: A poststorytelling survey assessed participant demographics. Two coders conducted tool-assisted (Dedoose v.8.3.35) thematic analysis on narrative storytelling videos and transcripts. Results: Storytellers were predominantly White (79/118, 66.95%), female (76/118, 64.41%), and non-Hispanic (109/118, 92.37%) with college/vocational education (50/118, 42.37%). Common themes were found related to living donation challenges for donors and recipients (eg, the fear of not being able to complete the LDKT process, of unsupportive family or rejected donation requests, and of unknown or adverse surgical outcomes and graft rejection) and recommended coping strategies (eg, seeking LDKT information, using prayer, and relying on a support network). Recipients provided advice that included being proactive and staying hopeful, whereas donors recommended seeking support, researching LDKT to comprehensively learn, and building a community of support. Limitations: Limited representation of diverse demographics. Conclusions: Although supplementary to traditional education about LDKT, digital storytelling provides a source of peer support that can enhance the experience of donors and recipients and encourage autonomy and self-management after transplant.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| 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".