Intervention to reduce unnecessary urinary catheter use in a large academic health science centre: A theory-based process evaluation
Bibliographic record
Abstract
Abstract Background: Inappropriate use of urinary catheters can increase the risk of catheter-associated urinary tract infections (CAUTI) leading to increased morbidity and increased costs. The overall purpose of this study was to evaluate an intervention to reduce unnecessary urinary catheter use and prevent CAUTI in hospitalized patients across a large academic health science centre. Methods: This was a two-phase study, which took place between 2017 and 2019. Phase 1 was a pre- and post-intervention design to test the impact of a CAUTI protocol across the organization. Audits on 4 units pre and post were conducted, and data were analyzed descriptively. Phase 2 was a theory-based process evaluation to understand the barriers and enablers to the implementation. Semi-structured interviews were conducted and then analyzed using a systematic approach. Results: Phase 1: All inpatients with urinary catheters admitted on the four selected units during the study period (n=99, pre) and (n=99, post) were included. CAUTI prevalence rate was 18.2% pre vs 14.1% post (p=.563). Phase 2: participants (n=18) who worked during the study period on the four audit units were interviewed, and a total of 13 barriers and 19 enablers were found across the participant groups. Conclusion: No statistically significant difference in CAUTI prevalence rates were noted. The theory-based process evaluation provided insights into barriers and enablers to the implementation which may help reformulate the intervention in the future.
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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 | 0.075 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".