Infection Prevention and Control in the Intensive Care Unit: Open versus Closed Models of Care
Bibliographic record
Abstract
In the intensive care unit (ICU), our sickest patients receive our most invasive treatments and are therefore highly vulnerable to hospital-acquired infection. Up to one-third of ICU patients develop infectious complications of care, with associated increases in morbidity, mortality, and healthcare costs. Earlier research has indicated substantial heterogeneity in uptake of infection prevention best practices in North American hospitals, and this variability may also exist in ICUs. We hypothesized that ICU system-level characteristics, including closed model of care, academic affiliation, and availability of a dedicated infection control practitioner (ICP), may be associated with improved infection prevention practices. During July 2011, we conducted a province-wide survey of nurse directors in ICUs across Ontario, Canada (population, 12 million). We developed a 77-item questionnaire to broadly capture ICU structures and processes relevant to infection prevention. The questionnaire was developed (item generation and reduction) by the authors and was further improved through pilot and sensibility testing by 3 ICU nurse directors and 2 ICPs. It was then distributed via e-mail by the Ontario Ministry of Health and Long-Term Care Critical Care Secretariat to nurse directors of all ICUs. A second email was sent to nonrespondents 2 weeks later. Approval was granted by the research ethics board at Sunnybrook Health Sciences Centre in Toronto, Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".