Impact of a Longitudinal Virtual Education Series on Home Dialysis Education
Bibliographic record
Abstract
Key Points Participation in the American Society of Nephrology Longitudinal Virtual Home Dialysis Program was associated with improved comfort levels in managing home dialysis. Nephrology fellowship training in home dialysis is sporadic and lacks structure. The HDU and ASN-HDU provides a structured curriculum that addresses many, but not all, gaps in home dialysis training during fellowship. Background Home dialysis has clinical and economic benefits, yet it remains underutilized in the United States. One barrier is a lack of experience among fellows due to inadequate training. To enhance fellow training, the American Society of Nephrology launched the Home Dialysis Virtual Longitudinal Education Program (ASN-HDU) with Home Dialysis University (HDU) in 2023. Methods We evaluated the ASN-HDU longitudinal program using mixed-methods. We sampled participants (P) from two groups: ( 1 ) fellows who attended both HDU and the American Society of Nephrology virtual program and ( 2 ) fellows who only attended HDU. We sent a post-HDU survey and 8-month follow-up survey to assess quantitative changes in comfort in home dialysis. We interviewed participants from both groups and used a constant comparative method to identify qualitative themes related to home dialysis training. Results After attending HDU, 52 participants reported median comfort levels of 7–8 of 10 for peritoneal dialysis topics and 4–5 of 10 for home hemodialysis topics. In the follow-up survey, only participants of ASN-HDU demonstrated significant improvement in comfort level for all home dialysis topics by 1–2 of 10 (except volume overload management on peritoneal dialysis). Two main themes emerged from 10 qualitative interviews: ( 1 ) home dialysis training is sporadic and lacks structure, with subthemes describing challenges with receiving hands-on training, lack of systematic training, and a need for fellows to create their own training pathways, and ( 2 ) HDU and ASN-HDU are structured curricula that fill many educational gaps, but not all, with subthemes describing the value of these programs, examples of how fellows apply knowledge gained in clinical practice, and gaps that these curricula cannot directly address. Conclusions Participation in ASN-HDU was associated with improved fellow comfort levels in home dialysis. Both HDU and ASN-HDU help address several gaps in home dialysis training in fellowship programs, but trainees may benefit from additional hands-on clinical experience.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".