Implementation of a central-line bundle: a qualitative study of three clinical units
Bibliographic record
Abstract
BACKGROUND: Evidence for the central line-associated bloodstream infection (CLABSI) bundle effectiveness remains mixed, possibly reflecting implementation challenges and persistent ambiguities in how CLABSIs are counted and bundle adherence measured. In the context of a tertiary pediatric hospital that had reduced CLABSI by 30% as part of an international safety program, we aimed to examine unit-based socio-cultural factors influencing bundle practices and measurement, and how they come to be recognized and attended to by safety leaders over time in an organization-wide bundle implementation effort. METHODS: We used an interpretivist qualitative research approach, based on 74 interviews, approximately 50 h of observations, and documents. Data collection focused on hospital executives and safety leadership, and three clinical units: a medical specialty unit, an intensive care unit, and a surgical unit. We used thematic analysis and constant comparison methods for data analysis. RESULTS: Participants had variable beliefs about the central-line bundle as a quality improvement priority based on their professional roles and experiences and unit setting, which influenced their responses. Nursing leaders were particularly concerned about CLABSI being one of an overwhelming number of QI targets for which they were responsible. Bundle implementation strategies were initially reliant on unit-based nurse education. Over time there was recognition of the need for centralized education and reinforcement tactics. However, these interventions achieved limited impact given the influence of competing unit workflow demands and professional roles, interactions, and routines, which were variably targeted in the safety program. The auditing process, initially a responsibility of units, was performed in different ways based on individuals' approaches to the process. Given concerns about auditing reliability, a centralized approach was implemented, which continued to have its own variability. CONCLUSIONS: Our findings report on a contextualized, dynamic implementation approach that required movement between centralized and unit-based approaches and from a focus on standardization to some recognition of a role for customization. However, some factors related to bundle compliance and measurement remain unaddressed, including harder to change socio-cultural factors likely important to sustainability of the CLABSI reductions and fostering further improvements across a broader safety agenda.
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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 | 0.025 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".