<i>Better without catheter</i>: the nationwide spread of a deimplementation strategy in clinical practice
Bibliographic record
Abstract
Many successful implementation studies fail to be sustained and spread after the publication. We aimed to spread a successful deimplementation strategy that reduced inappropriate peripheral venous catheter and urinary catheter use and evaluated the spread, adoption and effects of this strategy in clinical practice. We adapted the original successful study into a more accessible project, creating a toolkit called Better without catheter. We recruited 39 hospitals (more than half of all Dutch hospitals) across the Netherlands, which participated in regular online meetings. After 21 months, we sent an online survey to the project leaders of the participating hospitals to assess progress, barriers and facilitators to adopting the project. Widespread promotion and targeted emails were key factors in spreading Better without catheter. There was considerable variation in the hospitals’ progress; five had not yet started, six had completed the project and the others were at various stages in between. Major barriers included lack of time and resources, organisational facilities and the composition of local project teams. Key facilitators were organisational support and the involvement of physicians and nurse leaders. Project leaders valued the toolkit, the flexibility to tailor the project and the online meetings. Overall, the spread and adoption of this deimplementation strategy showed encouraging results, with 39 hospitals joining the network within 2 years. Although reach and engagement were high, the hospitals’ progress in the project was frequently hindered by organisational and management factors. Four elements supported the uptake: widespread promotion, the translation of the original study into an accessible improvement project with practical tools, the flexibility to tailor the approach locally and participation in a peer network.
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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 | 0.121 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 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".