New approaches to infection prevention and control: implementing a risk-based model regionally
Bibliographic record
Abstract
QUALITY ISSUE: Infectious disease outbreaks result in substantial inconvenience to patients and disruption of clinical activity. INITIAL ASSESSMENT: Between 1 April 2008 and 31 March 2009, the Vancouver Island Health Authority (Island Health) declared 16 outbreaks of Vancomycin Resistant Enterococci and Clostridium difficile in acute care facilities. As a result, infection prevention and control became one of Island Health's highest priorities. CHOICE OF SOLUTION: Quality improvement methodology, which promotes a culture of co-production between front-line staff, physicians and Infection Control Practitioners, was used to develop and test a bundle of changes in practices. IMPLEMENTATION: A series of rapid Plan-Do-Study-Act cycles, specific to decreasing hospital-acquired infections, were undertaken by a community hospital, selected for its size, clinical specialty representation, and enthusiasm amongst staff and physicians for innovation and change. Positive results were incorporated into practice at the test site, and then introduced throughout the rest of the Health Authority. EVALUATION: The changes implemented as a result of this study have enabled better control of antibiotic resistant organisms and have minimized disruption to routine activity, as well as saving an estimated $6.5 million per annum. When outbreaks do occur, they are now controlled much more promptly, even in existing older facilities. LESSONS LEARNED: Through this process, we have changed our approach in Infection Prevention and Control (IPAC) from a rules-based approach to one that is risk-based, focusing attention on identifying and managing high-risk situations.
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 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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".