Barriers and facilitators to vascular access point‐of‐care ultrasound in haemodialysis: An international survey of haemodialysis clinicians
Bibliographic record
Abstract
BACKGROUND: Utilising point-of-care ultrasound for assessment and cannulation of vascular access in people receiving haemodialysis has shown positive clinical results. Nonetheless, there is variation in how renal health care professionals worldwide embrace this method, and there's a lack of research on the factors that promote or hinder its adoption. OBJECTIVES: To explore regional differences, and barriers and facilitators, to the use of point-of-care ultrasound for assessment and cannulation of vascular access in haemodialysis. DESIGN: Exploratory descriptive cross-sectional web-based survey. PARTICIPANTS: Healthcare clinicians working in haemodialysis responsible for cannulation of arteriovenous fistula or grafts. RESULTS: The survey was completed by 645 health care clinicians from 38 countries. 75% to 93% of respondents from Australia/New Zealand, Canada, Europe and United Kingdom/Ireland reported access to ultrasound, compared to 26% (n = 43/167) from the United States. United States respondent's reported lower levels of ultrasound training than other regions. Facilitators for using ultrasound were: the availability of ultrasound training (87%, n = 558), to reduce miscannulations (76%, n = 255/336) and to improve patient outcomes (73%, n = 246/336). Point-of-care ultrasound barriers were lack of access to ultrasound education (82%, n = 196/239), lack of ultrasound machines (33%, n = 212/645) or believing that ultrasound was someone else's role (38%, n = 29/86). CONCLUSIONS: This study revealed national and regional differences related to haemodialysis point-of-care ultrasound. Understanding the regions requiring more education and implementation of ultrasound and what motivates staff, or deters from using ultrasound, is crucial for effectiveness of future implementation and workplace change initiatives.
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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 | 0.004 | 0.018 |
| 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) | 0.001 | 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".