Knowledge, skills, and confidence among healthcare staff in urinary catheterization
Bibliographic record
Abstract
INTRODUCTION: There is an increasing volume of urology referrals for urinary catheterization (UC). The aim of this study was to determine the confidence and knowledge among healthcare staff on UC. We also assessed their satisfaction with training and support received during catheter education and clinical practice. METHODS: This was a mixed-methods model using an anonymous, online survey circulated among all hospital staff. Weekly reminders were sent, quantitative data was obtained from closed-ended questions, and thematic analysis was performed for qualitative, open-ended questions. RESULTS: The response rate was 26% (n=90/350), from a heterogenous group of doctors and nurses from various specialties and grades in the hospital and community frequently dealing with UC. There was decreasing confidence levels in female UC (54%, n=47/87), three-way catheters (33%, n=29/89), and managing suprapubic catheters (25%, n=21/85). Female UC was reported as the most difficult of catheter insertions (35%, n=31/90). Although 83% (n=74/89) of respondents received catheter education, 53% (n=48/90) felt this was insufficient for clinical practice. Fifty-one percent (n=45/89) believed more support with UC in clinical practice was required and 64% (n=57/89) recommended changes in catheter education. The most common theme identified was the need for ongoing education and more practical supervision in clinical practice. CONCLUSIONS: Catheter training should focus on different types of catheters and management of difficult catheter scenarios. Standardizing safe catheter education during undergraduate training and including this as a part of regular annual or biannual mandatory training for healthcare staff involved in dealing with catheters in clinical practice would be the way forward.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".