Improving infection prevention practices through a novel safety coaches program
Bibliographic record
Abstract
Background: Healthcare-associated infections (HAIs) contribute to extended hospital stays and heightened in-hospital mortality rates. Effective infection prevention and control (IPAC) strategies are crucial for curtailing infection rates. The COVID-19 pandemic amplified disease burdens, compelling rigorous preventative measures. Mackenzie Health introduced the IPAC Safety Coaches Program to bolster adherence to IPAC guidelines and mitigate HAIs. Methods: IPAC Safety Coach training transpired over three iterative cohorts of educational sessions. Each session comprised a review of new learning material, discussion of key topics, and mandatory action items. Primary outcomes focused on hand hygiene and proper donning/doffing of personal protective equipment (PPE). Secondary outcomes focused on Clostridioides difficile (C. difficile) and central line associated bloodstream infection (CLABSI) rates. Data were collected using a standardized audit reporting form. Results: Across three training cohorts, improvements were noted in key compliance metrics: hand hygiene compliance improved by 9%, approaching statistical significance. Significant improvements were observed in PPE donning (19%), and PPE doffing (17%) compliance. Secondary outcomes demonstrated substantial reductions in C. difficile (55%) and CLABSI (57%) rates across the three cohorts. Conclusion: The novel IPAC Safety Coach Program led to improvements in hand hygiene, PPE donning and doffing compliance, and contributed to reduced C. difficile and CLABSI infection rates.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".