Identifying the Views and Needs of Family Physicians on Providing Care to Living Kidney Donors: A Cross‐Sectional Survey
Bibliographic record
Abstract
Optimizing the long-term care and follow-up of living kidney donors (LKDs) has been challenging, and prior LKDs have reported suboptimal healthcare experiences. Long-term care of LKDs is largely undertaken by primary care practitioners such as family physicians (FPs). We conducted a cross-sectional survey of Canadian FPs (n = 151). In our sample, 21.9% of participants reported that ≥1 patient had expressed interest in becoming a LKD, and 39.9% provided care to prior LKDs. While 55.5% knew how to find information on living kidney donation, 75.5% reported that information was not available in their practice. Only a minority had formal training in living kidney donation (<5%), and self-reported knowledge was low (median = 3 [scale 1 = not strong to 10 = very strong]). Knowledge improved significantly with educational activities, resources, experience, and practice needs. Attitudes toward living kidney donation were generally favorable with 71.5% stating that FPs should be involved in post-donation care. Clinical care guidelines (78.8%) were the most desired resource, followed by clear communication and reliable contact at transplant centers. Our findings inform the transplant community of an avenue to optimize LKD care by better-supporting FPs, who provide care to LKDs. This may enhance data collection on LKD outcomes and potentially increase donation rates.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".