Identifying Modifiable System-Level Barriers to Living Donor Kidney Transplantation
Bibliographic record
Abstract
Introduction: Studying existing health systems with variable living donor kidney transplantation (LDKT) performance and understanding factors that drive these differences can inform comprehensive system-level approaches to improve LDKT. We aimed to quantify previously identified barriers and estimate their association with LDKT performance. Methods: We conducted a cross-sectional survey of health professionals (HPs). Statements, rated on a Likert scale of "strongly disagree" to "strongly agree", captured themes related to communication; role perception; HP's education, training and comfort; attitudes; referral process; patient; as well as resources and infrastructure. The percentage who agreed with these statements was analyzed and compared by LDKT performance (living donation rates higher or lower than the national average) and participant characteristics. Results: We obtained 353 complete responses. Themes related to poor communication, poor role perception, and HPs education or training or comfort emerged as barriers to LDKT. When compared with HPs from high-performing provinces, those from low-performing provinces had lower odds of agreeing that their province promoted LDKT (adjusted odd ratio [aOR] = 0.27, 95% confidence interval [CI]: 0.16-0.48). They also had lower odds of initiating discussions about LDKT (aOR = 0.30, 95% CI: 0.17-0.55), and higher odds of agreeing that the transplant team is best suited to discuss LDKT (aOR = 2.64, 95% CI: 1.60-4.33) and that more resources would increase LDKT discussions (aOR = 2.06, 95% CI: 1.25-3.40). Nonphysician role and less than 10 years of experience were associated with the level of agreement across several themes. Creating guidelines, streamlining evaluations, and improving communication were ranked as priorities to increase LDKT. Conclusion: There are system-level barriers to LDKT and some were more prevalent in low-performing provinces. Interventions to eliminate them should be implemented in conjunction with patient-level interventions as part of a comprehensive system-level approach to increase LDKT.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".