The impact of simulation-based teaching on home hemodialysis patient training
Bibliographic record
Abstract
BACKGROUND: Simulation has been associated with positive educational benefits in the training of healthcare professionals. It is unknown whether the use of simulation to supplement patient training for home hemodialysis (HHD) will assist in improving a patient's transition to home. We aim to assess the impact of simulation training on home visits, retraining and technique failure. METHODS: Since February 2013, patients training for HHD are required to dialyze independently in a dedicated training room (innovation room) which simulates a patient's home prior to graduation from the program. We performed a single-center retrospective, observational, cohort study comparing patients who completed training using the innovation room (n = 28) versus historical control (n = 21). The outcome measures were number of home visits, retraining visits and technique failure. RESULTS: Groups were matched for age, gender, race, body mass index and comorbidities. Compared with controls, significantly more cases had a permanent vascular access at the commencement of training (57.1 versus 28.6%, χ(2) P = 0.04). Cases spent a median of 2 days [IQR (1.75)] in the innovation room. Training duration was not statistically different between groups {cases: median 10.0 weeks [IQR (6.0)] versus controls: 11.0 [IQR (4.0)]}. Compared with controls, cases showed a trend towards needing less home visits with no difference in the number of re-training session or technique failure. CONCLUSIONS: Simulation-based teaching in NHHD training is associated with a trend to a reduction in the number of home visits but had no effect on the number of re-training sessions or proportion of patients with technique failure.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".